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Doctor of Business Administration (DBA)

1-1-2013

An Exploratory Study of the Role of Values inMicroeconomic Decision-Making and theImplications for Organizations and LeadersHolly A. BellGeorge Fox University

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An Exploratory Study of the Role of Values in Microeconomic Decision-Making and the

Implications for Organizations and Leaders

Holly A. Bell

P.O. Box 2889

Palmer, AK 99645

[email protected]

Submitted to the School of Business

George Fox University

In partial fulfillment of the requirements

for the degree of

Doctor of Business Administration

Dr. Paul Shelton, Ph.D., Committee Chair

Dr. Ryan Halley, Ph.D., Committee Member

Dr. Dan Mertens, Ph.D., Committee Member

VALUES IN MICROECONOMIC DECISION-MAKING ii

TABLE OF CONTENTS

ABSTRACT ............................................................................................................................................................. VI

ACKNOWLEDGMENTS .................................................................................................................................. VII

CHAPTER 1 ............................................................................................................................................................. 1

INTRODUCTION ................................................................................................................................................... 1 RESEARCH QUESTIONS ........................................................................................................................................................... 1 HYPOTHESES ............................................................................................................................................................................ 2 CONSTRUCTS AND DEFINITIONS ........................................................................................................................................... 2 DELIMITATIONS ....................................................................................................................................................................... 5 SIGNIFICANCE OF THE STUDY ................................................................................................................................................ 6

CHAPTER 2

LITERATURE REVIEW...................................................................................................................................... 7

STATEMENTS OF FACT IN THE CLASSICAL LANGUAGE OF SCIENCE: .................................. 8

THE RATIONAL ACTOR ................................................................................................................................... 8 HISTORY OF THE FACT/VALUE DICHOTOMY...................................................................................................................... 9

APPLICATION PROBLEMS OF MECHANICAL-MATHEMATICAL ECONOMIC

MODELS ................................................................................................................................................................. 10

THE NEW SCIENCE .......................................................................................................................................... 13 LANGUAGE BEYOND “FACT” ................................................................................................................................................ 13 THE NEW ECONOMICS? ........................................................................................................................................................ 14

ORIGINS OF A POTENTIAL NEW ECONOMICS................................................................................... 16

A MODEL OF DECISION-MAKING BEYOND THE RATIONAL ACTOR ..................................... 19 EINSTEIN AND THE ETHER .................................................................................................................................................. 20 EINSTEIN MEETS ECONOMICS ............................................................................................................................................. 21

CLASSIFICATIONS OF STATEMENTS, THOUGHTS, AND INFORMATION ............................. 26 FACTS INFLUENCED BY VALUES .......................................................................................................................................... 27 VALUES INFLUENCED BY FACTS .......................................................................................................................................... 27 DISTINCTIVE INTEGRATION ................................................................................................................................................. 28 FULL INTEGRATION ............................................................................................................................................................... 28

ROLE OF RELATIONSHIPS, COOPERATION, AND THE INVISIBLE HAND ............................ 28 Figure 2.1. Bell’s ‘New Science’ Value-Inclusive Model of Economic Decision-Making ................................................. 29

FINAL THOUGHTS ON THE ‘NEW SCIENCE’ MODEL........................................................................................................ 30

ALTERNATIVE MODELS AND THEORIES OF VALUE-INCLUSIVE ECONOMIC

DECISION-MAKING .......................................................................................................................................... 31 SURAMANIAM’S FACT/VALUE DISTINCTION .................................................................................................................... 31

Figure 2.2. Subramaniam’s (1963) Diagram of a Model of a Perfect Rational Decision ..................................................... 32 THE MORAL/NON-MORAL NORMATIVE JUDGMENT DISTINCTION ............................................................................. 33 THE NORMATIVE-AFFECTIVE APPROACH......................................................................................................................... 35

Table 2.1 Adapted from William K. Frankena’s Kinds of Normative Judgments from Ethics (1973) .................................................. 36

KINDS OF NORMATIVE JUDGMENTS ................................................................................................................................... 36 Ethical or moral judgments proper: .............................................................................................................................. 36 Nonmoral normative judgments: ..................................................................................................................................... 36

VALUES IN MICROECONOMIC DECISION-MAKING iii

Judgments of moral obligation (deontic judgments): ................................................................................................................................ 36 Judgments of moral value (aretaic judgments): ........................................................................................................................................... 36 Judgments of nonmoral value: ............................................................................................................................................................................. 36 Judgments of nonmoral obligation: ................................................................................................................................................................... 36

BEHAVIORAL ECONOMICS/FINANCE ................................................................................................................................. 39 NEUROECONOMICS ................................................................................................................................................................ 41 NATURALISTIC DECISION-MAKING .................................................................................................................................... 41 OTHER ‘NONRATIONAL’ THEORIES AND MODELS ........................................................................................................... 42 FINAL THOUGHTS ON VALUE INCLUSIVE ECONOMIC DECISION-MAKING MODELS .................................................. 43

VALUE-INCLUSIVE MICROECONOMIC DECISION-MAKING ...................................................... 44

INDIVIDUAL VALUE DEVELOPMENT ..................................................................................................... 44 KOHLBERG AND VALUE DEVELOPMENT ............................................................................................................................ 45 THE NEO-KOHLBERGIAN PERSPECTIVE ............................................................................................................................ 47 SOCIAL LEARNING THEORY ................................................................................................................................................. 50 FINAL THOUGHTS ON INDIVIDUAL VALUE DEVELOPMENT............................................................................................ 53

Figure 2.3. Bell’s ‘New Science’ Value-Inclusive Model of Economic Decision-Making With

Neo-Kohlbergian & Social Learning Values Development ................................................................................. 55

VALUE-INCLUSIVE MICROECONOMIC DECISION-MAKING IN ORGANIZATIONS ......... 55

CHAPTER 3 ........................................................................................................................................................... 59

METHODS ............................................................................................................................................................. 59 SAMPLE ................................................................................................................................................................................... 59 INSTRUMENTATION .............................................................................................................................................................. 61 EXPERIMENTAL DESIGN AND PROCEDURES. .................................................................................................................... 63 PILOT TEST............................................................................................................................................................................. 64 DATA ANALYSIS ..................................................................................................................................................................... 65

Table 3.1 DIT-2 Measured Scores ................................................................................................................................................................................... 65

CHAPTER 4 ........................................................................................................................................................... 68

RESULTS ................................................................................................................................................................ 68

GROUP DESCRIPTIONS - UAA MAT-SU COLLEGE ............................................................................ 70 GROUP 1 .................................................................................................................................................................................. 70 GROUP 2 .................................................................................................................................................................................. 70 GROUP 3 .................................................................................................................................................................................. 71

GROUP DESCRIPTIONS - COLORADO STATE UNIVERSITY ......................................................... 72 GROUP 4 .................................................................................................................................................................................. 72 GROUP 5 .................................................................................................................................................................................. 73 DEMOGRAPHIC SUMMARY ................................................................................................................................................... 75

Table 4.1 Demographic Summary ....................................................................................................................................................... 75

LENGTH OF TIME IN COHORT .............................................................................................................................................. 76 Table 4.2 Length of Time in Cohort .................................................................................................................................................... 76

DIT2 MEASURED SCORES DATA ................................................................................................................ 77 MSC SCORES .......................................................................................................................................................................... 78

Table 4.3 MSC Postconventional Schema Score Descriptive Statistics................................................................................................................... 78 Figure 4.1. MSC PScore Posttest Figure 4.2. MSC PScore Pretest ................................................................................................ 79

VALUES IN MICROECONOMIC DECISION-MAKING iv

Table 4.4 MSC N2 Index Score Descriptive Statistics .................................................................................................................................................... 80 Figure 4.3. MSC N2Score Pretest Figure 4.4. MSC N2Score Posttest ......................................................................................... 80 Table 4.5 MSC Utilizer Score Descriptive Statistics ....................................................................................................................................................... 81 Figure 4.5. MSC Utilizer Pretest Figure 4.6. MSC Utilizer Posttest .............................................................................................. 82

CSU SCORES ........................................................................................................................................................................... 82 Table 4.6 CSU Postconventional Schema Score Descriptive Statistics ................................................................................................................... 82 Figure 4.7. CSU PScore Pretest Figure 4.8. CSU PScore Posttest ................................................................................................ 82 Table 4.7 CSU N2 Index Score Descriptive Statistics .................................................................................................................................................... 84 Figure 4.9. CSU N2Score Pretest Figure 4.10. CSU N2Score Posttest ....................................................................................... 84 Table 4.8 CSU Utilizer Score Descriptive Statistics ....................................................................................................................................................... 85 Figure 4.11. CSU Utilizer Pretest Figure 4.12. CSU Utilizer Posttest ......................................................................................... 86

ALL DATA SCORES ................................................................................................................................................................. 86 Table 4.9 All Data Postconventional Schema Score Descriptive Statistics ........................................................................................................... 87 Figure 4.13. All Data PScore Pretest Figure 4.14. All Data PScore Posttest ............................................................................. 87 Table 4.10 All Data N2 Index Score Descriptive Statistics ............................................................................................................................................. 88 Figure 4.15. All Data N2Score Pretest Figure 4.16. All Data N2Score Posttest ....................................................................... 88 Table 4.11 All Data Utilizer Score Descriptive Statistics ................................................................................................................................................ 89 Figure 4.17. All Data Utilizer Pretest Figure 4.18. All Data Utilizer Posttest ............................................................................ 90

NUMBER OF VALUES SHARED SCORES .............................................................................................................................. 90 Table 4.12 Shared 10+ Postconventional Schema Score Descriptive Statistics ..................................................................................................... 91 Table 4.13 Shared 5 Postconventional Schema Score Descriptive Statistics ........................................................................................................... 91 Figure 4.19. 10+ PScore Pretest Figure 4.20. 10+ PScore Posttest ............................................................................................... 92 Figure 4.21. 5 PScore Pretest Figure 4.22. 5 PScore Posttest .......................................................................................................... 92 Table 4.14 Shared 10+ N2 Index Score Descriptive Statistics ...................................................................................................................................... 93 Table 4.15 Shared 5 N2 Index Score Descriptive Statistics ............................................................................................................................................ 93 Figure 4.23. 10+ NScore Pretest Figure 4.24. 10+ NScore Posttest ............................................................................................. 94 Figure 4.25. 5 NScore Pretest Figure 4.26. 5 NScore Posttest ........................................................................................................ 94 Table 4.16 Shared 10+ Utilizer Score Descriptive Statistics ......................................................................................................................................... 95 Table 4.17 Shared 10+ Utilizer Score Descriptive Statistics ......................................................................................................................................... 95 Figure 4.27. 10+ Utilizer Pretest Figure 4.28. 10+ Utilizer Posttest ............................................................................................. 96 Figure 4.29. 5 Utilizer Pretest Figure 4.30. 5 Utilizer Posttest........................................................................................................ 96

RESULTS SUMMARY .............................................................................................................................................................. 98 Table 4.18 Results Summary .................................................................................................................................................................... 98

CHAPTER 5 ........................................................................................................................................................... 99

DISCUSSION ......................................................................................................................................................... 99 POSTCONVENTIONAL AND N2 SCORES .............................................................................................................................. 99 NORM COMPARISONS POSTCONVENTIONAL AND N2 SCORES ................................................................................... 100 NON-NORMED FACTORS FOR POSTCONVENTIONAL AND N2 SCORES ...................................................................... 102 UTILIZER SCORES ............................................................................................................................................................... 106 RESEARCH CONCLUSIONS ................................................................................................................................................. 108 LIMITATIONS ....................................................................................................................................................................... 110 IMPLICATIONS FOR ORGANIZATIONS AND LEADERS .................................................................................................... 112

VALUES IN MICROECONOMIC DECISION-MAKING v

IMPLICATIONS FOR ACADEMICIANS ................................................................................................................................ 115 RECOMMENDATIONS FOR FUTURE RESEARCH .............................................................................................................. 116 CONCLUSION........................................................................................................................................................................ 117 REFERENCES ....................................................................................................................................................................... 121

APPENDIX A

DEFINING ISSUES TEST-2 AND DEMOGRAPHIC QUESTIONS

PRE-TEST ........................................................................................................................................................... 137

APPENDIX B

DEFINING ISSUES TEST-2

POST-TEST ......................................................................................................................................................... 151

APPENDIX C

VALUES/ANTI-VALUES PROMPTS ......................................................................................................... 160

APPENDIX D

VALUES EXPRESSED .................................................................................................................................... 161

APPENDIX E Shared Values Set: Group 1 ........................................................................................................................................... 163 Shared Values Set: Group 2 ........................................................................................................................................... 164 Shared Values Set: Group 3 ........................................................................................................................................... 165 Shared Values Set: Group 4 ........................................................................................................................................... 166 Shared Values Set: Group 5 ........................................................................................................................................... 167

APPENDIX F

N2 SCORE DESCRIPTIVE STATISTICS AND T-TEST RESULTS BY GROUP ......................... 168

APPENDIX G

STAGE 23 AND 4P DESCRIPTIVE STATISTICS AND T-TESTS ..................................................... 173

APPENDIX H

GEORGE FOX UNIVERSITY HUMAN SUBJECTS COMMITTEE APPROVAL ....................... 188

VALUES IN MICROECONOMIC DECISION-MAKING vi

Abstract

This research is designed to test the role reflecting on and sharing values plays in our

individual decision-making schemas in a group. The research is based on evidence from

the literature that values play a role in economic decision-making, can be formed and

utilized either consciously or unconsciously, and impact microeconomic decision-making

and ethics in organizations. The study found that when decision considerations were

reframed in a values context the decision-making process became more quasi-rational,

but the decisions participants made were as good or better than they were before values

were introduced. In some cases decision-makers became less interested in personal

considerations after decisions were framed in a values context. This is an important

finding because traditional models of economic decision-making assume the decision-

making process is always rational and decision-makers are always self-interested. There

may also be some relationship between utilizing values and improved ethical decision-

making for women within small groups with relatively strong relationships in a

community.

Keywords: economic decision-making, schemas, values, values in decision-

making, organizational behavior.

VALUES IN MICROECONOMIC DECISION-MAKING vii

Acknowledgments

The hard work of a dissertation is often traversed alone in a darkened room lit

only by a computer screen while everyone else is asleep. This does not mean I was not

supported and I would like to acknowledge those who have been by my side during this

journey.

First, I would like to acknowledge Drs. Paul Shelton, Ryan Halley, and Dan

Mertens who served as my dissertation committee. Your comments and critiques were

helpful, encouraging, challenging, and intellectually stimulating. I never felt your

intention was to make me fail, but only to help make me a better researcher. I also want

to thank Dr. Paul Shelton for allowing me to use class time with his students to conduct

experiments at Colorado State University.

I want to thank my husband, Eldon, who put up with eating a lot of oatmeal for

dinner over the past several years and didn’t complain once. We also had to give up many

of the activities we enjoy doing together and I promise, we will learn to have fun again.

I also want to acknowledge my colleagues at the Mat-Su College of the

University of Alaska Anchorage for their support and encouragement. You were very

understanding when I declined to take on a project or sit on a committee and many of you

generously offered your class time so I could run the experiments used in this

dissertation.

I would also like to thank Ian, Shad, Belle, and Estle, my GFU cohort, who have

kept me going throughout the program and the dissertation process. Our regular Skype

calls have been a great place to compare notes, get help and, at times, vent. You are the

only people who will ever really understand.

VALUES IN MICROECONOMIC DECISION-MAKING 1

An Exploratory Study of the Role of Values in Microeconomic Decision-Making and the


Chapter 1

Introduction

Much of the traditional economic decision-making models that are still in use

today are based on the assumptions of rational choice and self-interest. These

assumptions create a fact/value dichotomy. However, there is evidence that while

assumptions of rationality and self-interest are normative constructs, the way we actually

make decisions incorporates both facts and values (Bell, 2011b; Binmore, 2007; Nelson,

2003; Putnam, 2002). The purpose of this exploratory research is to test the role

reflecting on and sharing values plays in our individual decision-making schemas in

groups. Specifically, can introducing values into decision considerations disrupt existing

unconscious schemas? Implications for microeconomic decision-making in organizations

will also be considered.

Research Questions

The framework of this exploratory study was based on the following research

questions:

What role do values play in microeconomic and other decision-making?

How does the reflection on and sharing of personal values associated with

decisions impact future decisions?


How does sharing personal values with others impact their decision-making

schemas?

Hypotheses

This study used an instrument designed to measure unconscious schemas in

ethical decision-making using a pre- and posttest design. The instrument, the DIT2, has

proven very reliable and has been used in over 400 published studies (see “Instrument”

section of this paper). Extensive use of the DIT2 has found that pre- and posttest results

without any intervention have proven to be the same with no statistically significant

movements in scores (Rest, Narvaez, Thoma, & Bebeau, 2000). Based on that data we

can assume that if the planned intervention in this experiment has had no impact on

decision-making, the statistically significant differences in the mean values of any

measured data pre- and posttest will be 0. Given this information the hypotheses are as

follows:

H0: D = 0

H1: D ≠ 0

Where D is the difference between mean DIT2 scores.

Constructs and Definitions

The discussion of value-inclusive economic decision-making introduces

constructs that not everyone might be familiar with. For purposes of this study the

following definitions were utilized:

Decision considerations. A decision consideration is a matter weighed or taken

into account when forming a decision.


Opportunity set. The opportunity set in decision-making is defined by Sen

(1994) as “the anticipated set of alternative outcomes…which the person reckons she can

have through different choices of her strategy” (p. 385). One example would be a

manager who wishes to solve a problem in which an employee has excessive absences

(the strategy is to solve the absence problem). The opportunity set is the set of alternative

actions the manager believes he or she can take to solve the problem. The opportunity set

can be influenced by factors other than rational choice like ethics and epistemology

(within the values ether) causing “menu-dependency”.

Menu dependence. When the opportunity set is influenced by ethics, social

behavior, epistemology, or other non-rational choice influences that narrows the

opportunity set. A good example of menu-dependent behavior is choosing not to take the

last apple in a bowl (Sen, 1994).

Decision set. The decision set is the actual outcome chosen from the opportunity

set. In the example above it would be the action the manager actually takes (or intends to

take) to solve the problem. This could be a single decision by a single person, but is most

often used to describe the aggregate of decisions made by a number of individuals facing

a similar problem or situation. The decision and its impacts may influence future

opportunity sets and decisions (Bell, 2011b; Sen, 1994).

Personal Values. These are a type of value to which an individual is committed

and which influences his or her behavior (Kaushal & Janjhua, 2011; Theodorson &

Achilles, 1969).


Schemas. The group of values/ethics/morals an individual unconsciously

possesses in long-term memory that structure and guide an individual’s thinking and

decision-making.

Values. Several short definitions of values exist including:

“Values can refer to the desired or to the desirable, and the two are not

equivalent…values determine our subjective definition of rationality”

(Hofstede, 2001, pp. 1-6).

Prescriptive statements of what ought to be (Putnam, 2002).

However, for this study we used Hall, Guo, and Davis (2002) more comprehensive

definition. They define values as:

…cognitive scripts or cognitive maps or as value schemata (determinants

of action)…[values] define the primary perspective that an individual uses

to make sense of a new problem scenario and to generate solutions. These

values generate perspectives that fundamentally restrict the way the

individuals ‘see’ the world, interpret information, and make decisions…

there are six types of personal values (perspectives) that individuals

exhibit…theoretical, social, political, religious, aesthetic, and economic.

(pp. 2-3)

Values can also be used as a form of persuasion in “situations of practical

reasoning”, which is consistent with the assertions of this paper. Bench-Capon

(2003) describe the role of values in these cases is to “persuade rather than to

prove, demonstrate or refute” (p. 429). He further states that:


…persuasion in such cases relies on a recognition that the strength of an

argument depends on the social values that it advances, and that whether

the attack of one argument on another succeeds depends on the

comparative strength of the values advanced by the arguments concerned.

(p. 429)

It is also important to note that the terms values, morals, and ethics are

often used interchangeably to express the same construct. For purposes of this

paper they are considered the same.

Values ether. It is the unseen medium that binds together all the value factors that

lead us to a decision.

Values reflection. Refers to internally contemplating the values associated with

decision considerations.

Values sharing. Refers to sharing the values you have assigned to decision

considerations with others.

Delimitations

While the types of factors beyond rational self-interest that individuals consider

are numerous, this study will be delimited to values. Specifically, whether reflection on

and sharing of values related to decision-making criteria impacts the way in which

individuals make ethical decisions in a group.

As the types of economic decisions individuals make are numerous the scope of

the study will be delimited to microeconomic applications for individual value-inclusive

decisions in groups.


Significance of the Study

Much has been written about the mechanical-mathematical models of economic

decision-making based on assumptions of rational self-interest and utility maximization

for both individuals and organizations, yet little attention has been given to the empirical

study of the role of values in individual decision-making in groups and how this might

impact microeconomic decision-making in organizations. Specifically, whether reflecting

on and sharing values associated with decision considerations changes the unconscious

schemas individuals’ develop that influence their decision-making. If these schemas can

be disrupted, it provides support for the influence of group, societal, environmental, and

organizational values in microeconomic decision-making creating implications for

organizations and managers.

This research is timely as academicians and practitioners are looking for ways to

incorporate (and explain) behavioral and other factors in decision-making that go beyond

rational choice (Pressman, 2005; Putnam, 2002; Sen, 1994, 2004, 2005; Thaler &

Sunstein, 2009; ). Some less prominent, yet equally antithetical theories seek ways to

create more organic models and incorporate complex systems and ‘new science’ to

explain observations of human decision-making behavior (Bozicnik & Matjaz, 2009;

Mikhalevskii, 1971; Wheatley, 2006).


Chapter 2

Literature Review

This literature review begins by developing a theory of value-inclusive economic

decision-making that will be used as the basis for this research. Alternative models and

theories of value-inclusive economic decision-making from the literature will also be

reviewed. The literature review concludes with a more specific discussion of value-

inclusive microeconomic decision-making including individual value development and

the importance of value-inclusive microeconomic decision-making in organizations.

Toward Value-Inclusive Economic Decision-Making1

Much of contemporary economic thought seeks to remove all value judgments

from the discussion of economic decision-making in favor of the rational choice

tautology. Considerations of the “softer” social science aspects of economic thought have

been replaced with mechanical-mathematical models in an effort to move the discipline

closer to the hard sciences (Binmore, 2007; Nelson, 2003; Putnam, 2002). In order to

allow empirical testing of the established mathematical models, the assumption of

rational actors is required. To address the issues of complexity associated with economic

decision-making that makes absolute rationality problematic, many economists consider

the rationality assumption to be bounded rationality; limited by time, available

information, and cognitive ability.

1 Note: This portion of the paper was originally published open source in 2011 in the European Journal of

Social Sciences, 21(4), 638-649. The publisher has granted permission for use of the manuscript by the

author for educational and non-profit purposes. It has been modified for this paper, but remains

significantly as originally published.


The purpose of this section is to explore whether there is a more holistic and

organic approach to economic theory that allows for the coexistence of facts and values

within economics while simultaneously moving closer to the hard sciences, specifically

the natural and physical sciences.

Statements of Fact in the Classical Language of Science:

The Rational Actor

As discussed previously, the utilization of scientific mathematical models requires

the assumption of rational actors in economic decision-making. This assumption has

become problematic for many scholars (Angner & Loewenstein, 2007; Putnam, 2002;

Tideman, 2005; Yuengert, 2000). Do individuals always behave rationally? Does one

person define rationality the same as another? If a decision is rational for one person is

the same decision also rational for another? Most people can look at anecdotal evidence

in our own lives that indicates people do not always act in a rational manner. Many

factors such as stress, emotions, experience, and moral or ethical values can all impact

“rational” decision-making.

The language of science does not allow for the consideration of these

psychological or value factors in the analysis of economic decision-making. Value

statements are viewed as subjective, while “fact” statements are considered objective and

appropriate for scientific analysis. In science “matters of fact” are considered statements

that describe what “is” while “relations of ideas” (values) are prescriptive statements of

what “ought” to be (Putnam, 2002). Putnam describes the split of values and facts in the

“scientific” study of economics as going beyond a distinction to a jointly exhaustive and


mutually exclusive dichotomy that does not allow facts and values to coexist within the

analysis.

History of the Fact/Value Dichotomy

Aristotle first proposed a distinction between facts and values, which he described

as positive (what is) and normative (what one should do) inquiry. However, until the

early twentieth century positive and normative inquiry within the sciences, while

distinctive, coexisted in a hierarchical relationship. Ethics/prudence was “by nature above

all other disciplines”. This normative inquiry made use of the subordinate inquiries “in

pursuit of the highest human ends, and was in turn the justification and motivating force

behind the inquiries of the various subordinate sciences” (Yuengert, 2000, p. 1).

The fact/value dichotomy of the early twentieth century created problems

regarding how “facts” should be defined within the language of science. Putnam (2002)

provides a comprehensive discussion of these issues summarized in the following

paragraphs. The logical positivism that emerged at the beginning of the twentieth century

believed that judgments fell into one of three classifications:

1) “Synthetic” judgments were those that were empirically verifiable or falsifiable.

2) “Analytic” judgments were true [or false] on the basis of logical rules alone.

3) “Cognitively Meaningless” judgments included ethical, metaphysical, and

aesthetic judgments. These value judgments were not considered within the field

of science.

Distinguishing between synthetic and analytic judgments was problematic in part

because there was a difference of opinion about whether the truths of mathematics were

analytic or synthetic. Kant believed mathematics is both synthetic and a priori while the


logical positivists believed the principles of mathematics are analytical. When the

assumption (as discussed by Putnam, 2002) that the principles of mathematics are

synthetic is removed, there becomes a wide range of ordinary distinctions (both analytic

and purely descriptive) available.

Another problem raised by the logical positivists was that in order for the

synthetic/analytic distinction to be true, it must work when applied to every statement of

theoretical physics. For example, we must ask if the Principle of the Conservation of

Energy is analytic or synthetic in order to fully “rationalize” physics. This proved to be

problematic, as atoms could not be “observed” before microscopes; and physics moved

into the areas of relativity theory and quantum mechanics. Yet many scientists believed

that a scientific statement of fact must be “conclusively verifiable by confrontation and

direct experience” (Putnam, 2002, p. 22). The language of science continued to insist,

“the predicates admitted into the ‘factual’ part of the language of science had to be

‘observational terms’ or reducible (by specified and limited means) to observation terms”

(Putnam, 2002, p.23). The “reductionistic unholistic view” (Bozicnik & Matjaz, 2009) of

science made discussions about bacteria, electrons, charges, or the gravitational field

irrational.

Application Problems of Mechanical-Mathematical Economic Models

As the previous discussion of Putnam’s work emphasizes, the language of the

hard sciences seeks rational outcomes based on statements of fact that are empirically

measurable. This requires the dichotomy of facts and values. However, defining “facts” is

problematic even in the hard sciences.


In an effort to allow “scientific” mathematical analysis of economics, the

assumption must be made that the actors involved act in a rational, self-interested manner

with factual judgments separated from value judgments in economic decision-making.

The implication of the assumption of rationality is that actors will seek to maximize

utility through their economic decision-making. This also assumes that they have all

information required to make the optimal decision, have not learned from previous

experiences, and are not influenced by other people; rationality is assumed to be inherent

(Putnam, 2002).

Researchers in strategic behavior have found problems with these assumptions

when using game theory to study rational behavior in small groups where individual

actors can impact the well-being of others. The results found that multiple equilibria were

possible and that a “very high order” of rationality was needed to determine an

individual’s optimal strategy. The studies also found that learning and natural selection

can have an impact on optimal behavior in practice (Schmalensee, 1991). Other

experiments found that while individual “reasoners” behave intelligently, they do behave

differently than the theory of pure economic rationality would expect (“Philosophy of

Economics,” 1998)

To address the issue of the inability of individual actors to consistently act in a

rational manner, the subject of economics was divided into micro-and macroeconomics to

accommodate (and inspired by) Keynes and his General Theory (Groenewegen, 2003).

Keynes believed that economic behavior should be measured at the aggregate level and

not the “atomic” or micro level of neoclassical theory (Togati, 2001). Using the rational,

mechanical, mathematical, and fact driven structure of the hard sciences made discussion


of economics at the “atomic” level as difficult as it did for physics. However, Keynes’

approach was consistent with the increasingly narrow specialization within the hard

sciences (Bozicnik & Matjaz, 2009).

The mechanical aspects of the classical language of science are also problematic

when applied to the study of economics. The mechanical, fact driven model of classical

science creates a relatively closed system of exploration. As a result, economic models

utilizing this system end up closed within the confines of science. An extensive list of

assumptions, described by Mikhalevskii (1971), are required to fit within the confines of

a closed, mechanical system. Among them are the assumptions of “a consistent, stable,

and …constant system of values” and conformity to the utility maximization criterion as

the only constant and final goal. Mikhalevskii also finds “the narrowness of the

statistical and dynamic definition of individual and social motivation” to be problematic

(pp. 7-8). Mechanical models fail to consider relationships and their impact on the overall

economic system. He states there is no:

…mechanism for explaining internal conflicts in the process of development

(except competition) on the basis of the influence of the environment and the

internal structure of the very system through direct relations and feedback and

compounding relations based upon them… (p. 8)

There is also no mechanism to measure the impact of individuals on the system when

utilizing a Keynesian macroeconomic approach (Bozicnik & Matjaz, 2009).


The New Science

Language Beyond “Fact”

As previously discussed, the classical language of science, using restrictive

statements of fact, made discussion about bacteria, electrons, charges, or the gravitational

field irrational. This became increasingly problematic as “new science” emerged in

biology, evolution, chaos theory, relativity theory, and quantum physics. Suddenly

science was forced to look at the world in a different way. The world began to appear less

mechanical and orderly, and more creative, dynamic, and engaged in continuous change

while maintaining order. (Wheatley, 2006)

Ganley (1995) discusses the revolutionary fervor of theoretical physics at the

beginning of the twentieth century. Areas of expanded theoretical interest for scientists

included quantum physics, the special and general theories of relativity, a theory of the

inner workings of the atom, Heisenberg’s uncertainty principle, quantum mechanics, and

the early stages of research in quantum electrodynamics. Research in the “new science”

created changes in methodology for scientists. The world around them was no longer

viewed as strictly mechanical and outside our influence. Ganley quotes Albert Einstein

regarding the new physics: “physical concepts are free creations of the human mind, and

are not, however, it may seem, uniquely determined by the external world” (p. 397).

Establishment of the new language of physics also required a new way of thinking

about the world. Scientists were forced to look beyond “facts” to possibilities,

probabilities and not just predictions. They came to realize that the natural world did not


always behave the same way twice, yet maintained orderliness (Wheatley, 2006). As

Fritjof Capra (1983) stated:

In their struggle to grasp this new reality, scientists became painfully aware

that their basic concepts, their language, and their whole way of thinking were

inadequate to describe atomic phenomena. Their problem was not only

intellectual but involved an intense emotional and existential experience… (p.

76)

One example included experiments that determined electrons behave in an inconsistent

manner. Sometimes they behave like particles (matter) and at other times they behave

like waves (energy) (Bozicnik & Matjaz, 2009).

Bozicnik & Matjaz (2009) describes the evolution of scientific thought moving

from determinism, to interdependence, to dialectical dynamics that recognize the “unity

in diversity of everything around us” (p.347).

The New Economics?

While changes in language and methodology were being made in the scientific

community to change the way natural phenomena were discussed, predicted, and

described, the economic discipline was slow to respond. Noted historian and philosopher

of economic thought, Philip Mirowski, believed that neoclassical economics was based

on mid-nineteenth century physics that clung to outdated mathematical techniques that

did not seek to make economics like science, but “a mathematically rigorous discipline”

(Ganley, 1995, p.398). Science had evolved, with scientists like Einstein allowing for

nonobservable factors (Togati, 2001). However, the study of economics has not evolved

in the same manner primarily in the name of mathematical rigor.


Economists agree that mathematics is still important to “provide a social-scientific

basis for understanding, explaining, and, perhaps, predicting economic phenomena”

(Routledge, 1998, para 10). However, when economists ask a question such as: “Are

inflation and unemployment related?” they may be able to use mathematical models to

answer yes or no, but economists need to go beyond mathematics to explain the often-

unobservable causes. On this topic the Routledge Encyclopedia of Philosophy of Sciences

states:

…the approach to economic theorizing that stipulates that the discipline is

purely formal will not aid in shedding light on these real, though

unobservable, economic mechanisms. On this line of thought, the persistent

mathematization of economics ought to be construed as a means to an end

rather than the end itself. The formal or mathematical machinery of economics

is intellectually valuable only insofar as it contributes to a better

understanding of real, empirically given economic processes, causes, and

systems. (para. 10)

The “new economics” should look beyond the assumptions of economic actors

being rational, self-interested, and autonomous maximizers required to fit the science of

economics into the “Newtonian idea of a clockwork world” (Nelson, 2003, p. 5). Looking

beyond requires economists to include unobservable factors such as values, ethics,

expectations, motivations, culture, and the impact of relationships and cooperation on

economic decision-making.


Origins of a Potential New Economics

This paper has discussed how the origins of the neoclassical school of economic

research was found in mid-nineteenth century physics (Ganley, 1995) as economists

sought to utilize the same mathematical methodology. The early twentieth century

brought a division of micro- and macroeconomics to accommodate Keynes by removing

the problems associated with the rational explanation of both science and economics at

the “atomic” level (Groenewegen, 2003). While the methodology of science was

evolving as new fields of inquiry emerged, the field of economics did not respond in a

similar manner. Some scholars might argue that Keynes was using Einstein’s approach to

the theory of relativity when he developed his General Theory of Employment, Interest,

and Money, (Togati, 2001) others, including this author, see applications to Einstein’s

theory of relativity that are quite different. If the origins of the new economics didn’t

reside with Keynes, where did (or will) they come from? The answer to this question

requires revisiting the origins of the science of economics.

The first attempt to establish an analytical form of economic science was made by

François Quesnay and a group of French statesmen and philosophers in the mid-

eighteenth century. The foundation of their policy was obedience to Nature (Marshall,

1890/1920). However, as Marshall goes on to explain, these early economists lost their

way when they attempted to incorporate the scientific methods of the physical sciences:

…there was much in the tone and temper of their treatment of political and

social questions which was prophetic of a later age. They fell however into a

confusion of thought which was common even among scientific men of their


time, but which has been banished after a long struggle from the physical

sciences. They confused the ethical principle of conformity to Nature, which

is expressed in the imperative mood, and prescribes certain laws of action,

with those causal laws which science discovers by interrogating Nature, and

which are expressed in the indicative mood. (Marshall, 1890/1920; Appendix

B.7)

Statements of fact rather than signals of direction continue to dominate economic

thought in the twenty-first century. Another important point to note regarding the

structure of the emerging economic science as described by both Quesnay and Adam

Smith is that the micro- and macroeconomic elements were blended and merged, treating

the subject as a whole without artificial distinctions. The intellectual climate also allowed

for positive and normative economics to exist simultaneously. Smith, and later Marshall,

blended their discussion of economics with a mix of facts and theories (Bozicnik &

Matjaz, 2009; Groenewegen, 2003).

After Quesnay, Marshall credits Adam Smith as having the next great step in

advance within the discipline of economics. The very title of Smith’s major work, An

Inquiry Into the Nature and Causes of the Wealth of Nations, implies economic systems

are natural phenomena. It also calls for the exploration of causation, not simply factual

description. He also recognizes the unobservable by noting that while man may attempt

to control these natural economic systems, they continue to be “led by an invisible hand

to promote an end which was not part of his intention” (Smith, 1776/1904; para. IV.2.9).

Smith (1776/1904) was also concerned with the “evolutionary factors in

explaining economic development,” which included discussion about the nature of


society and government, and the role of culture and the arts. Smith saw economic systems

as dynamic and constantly changing (Bozicnik & Matjaz, 2009; Groenewegen, 2003). He

also recognized the role of “moral” and “natural” sentiments of economic actors in

decision-making (Smith, 1759/1790).

Sen (2004) discusses a deeper analysis of Adam Smith’s work that demonstrates

that Smith did not believe self-interest was the only motivator of people. He states: “…he

discussed extensively the prevalence and the important social role of such values as

sympathy, generosity, public-spiritedness and other affiliative concerns” (p.9). In another

work Sen (1994) discusses that the pioneers of utility theory (including John Stuart Mill,

William Stanley Jevons, Francis Y. Edgeworth, and Alfred Marshall) explicitly accepted

a variety of motivations for economic decision-making.

Based on this discussion it is fair to conclude that early economic theorists

believed that the discussion of economics belonged within the context of our natural and

holistic world, which includes multiple motivations for economic decision-making. The

science of economics lost this framework of discussion when it moved toward the

increasingly factual language of the physical sciences. The language and methodology of

the sciences changed at the beginning of the twentieth-century, while the field of

economics remained trapped within the mid-nineteenth century model. However,

returning to the origins of economic science reveals greater parity with the “new science”

than current mechanical-mathematical models. Based on this evidence, is it possible to

build a theoretical framework for the discussion of economic decision-making that is

consistent with both classical economic thought and the new science?


A Model of Decision-Making Beyond the Rational Actor

While the discussion of economic models within the context of classical

economic thought and the new science has broad applications, the limited space of this

paper requires the scope of this discussion to be limited to economic decision-making.

While the discussion thus far has focused on a potential “new economics,” it is probably

necessary to further define and identify what that encompasses. To be consistent with

contemporary scientific thought, this paper will use Bozicnik and Matjaz (2009)

description of a holistic, interdependence-based system that recognizes the “unity in

diversity of everything around us.” This “new science” view of economic systems allows

for:

1) Inclusion of the unobservable.

2) Recognition of complex systems.

3) The ability of the individual “atomic” actor to influence the system and

determine “reality” with their interventions.

4) The ability of the system to influence the actor.

5) Multiple equilibria and inconsistent behavior.

6) Recognition of the role of relationships and cooperation in economic behavior.

7) Inclusion of values/ethics/morality.

8) Inclusion of information and learning in decision-making.

This view is also consistent with classical economic thought in that it looks at

economic systems holistically with no division between micro and macro elements, or

positive and normative statements. Fact and theory are allowed to coexist. It allows for

the inclusion of “sentiments” (values, ethics, and morals), the unobservable (i.e. the


invisible hand), the exploration of causation across a broad spectrum of possibilities, and

allows for “evolution” of economic systems with changing cause and effect that can

include multiple goals and objectives over time.

Einstein and the Ether

The major inspiration for the value-inclusive economic decision-making model

discussed later in this paper was an address delivered on May 5th

, 1920, in the University

of Leyden by Albert Einstein entitled Ether and the Theory of Relativity. This insight into

Einstein’s views of the “new science” has strong parallels with a potential theoretical

model for economic decision-making.

In his address, Einstein rejects Newton’s notion of dualism in nature, both in

general and as it relates to the theory of gravity. Specifically, that there can be “reciprocal

action only through contact, and not through immediate action at a distance” (Einstein,

1920, p. 1). To solve this problem, and to unify the view of the nature of forces, Einstein

supports the existence of an “ether.” The ether is an inert medium that fills up universal

space and conveys forces by elastic deformation of the medium. This explains how

movement is possible with both direct contact (mechanical and seen) and distant contact

(non-mechanical and unseen). This is much like a boat being able to be moved through

the water either by pushing it (direct contact) or by the ripples created by the wake of

another boat (distant contact).

The ether allows both seen mechanical forces like densities, velocities, and

stresses to coexist with the unseen electric and magnetic forces, and abandons the

dualism that existed. He also states: “the ether of the general theory of relativity is a


medium which is itself devoid of all mechanical and kinematical qualities, but helps to

determine mechanical (and electromagnetic) events” (Einstein, 1920, p. 4). Einstein also

describes ether as being indistinguishable from ponderable matter, which, at least in part,

subsists in the ether. Within the theory of relativity the state of the ether is “at every place

determined by connections with the matter and the state of the ether in neighbouring

places…” (p. 5). In other words, the state of the ether is determined by its relationships

with the matter and its state relative to the states around it.

While the ether was necessary for Einstein’s General Theory of Relativity, he is

said to have rejected it later when he developed his Special Theory of Relativity.

Hawking (2001) states that Einstein believed that the notion of an ether was “redundant,”

as proposed in a 1905 article. Yet in the 1920 address discussed here he does not reject

the notion of an ether, simply a change in his conception of it. Einstein states: “More

careful reflection teaches us, however, that the special theory of relativity does not

compel us to deny ether” (p. 3). Einstein believed we could continue to assume the

existence of an ether, but we must not ascribe to it a definite state of motion, removing all

mechanical characteristics as discussed earlier in this section. He does believe, however,

that the ether can still be characterized as a medium.

Einstein Meets Economics

The problem with dualism in nature faced by Einstein parallels with the dualism

problem associated with the fact/value dichotomy of current economic thought. The

assumption of the dichotomy is that economic statements can be made through fact alone

and not through the influence of values. According to Hume, we cannot determine an

“ought” from an “is” (Putnam, 2002). However, to be consistent with the new science,


the fact/value dichotomy should be removed because “evaluation and description are

interwoven and interdependent” (Pressman, 2005; p. 485)

The values ether. To unify this view of economics the existence of a “values

ether” as a medium through which statements, thought, and information move is

proposed. Einstein (1920) describes the ether as both conditioning the behavior of inert

masses, and being conditioned by them. In the economic decision-making process (which

moves through the “values ether”), the ether not only conditions our decisions, but is also

conditioned by our decisions. Also consistent with Einstein’s theory, economic decisions

are partially conditioned by decisions outside the territory under consideration (see

Einstein, 1920, p. 4). While the decisions themselves do not reside within the values

ether, the statements, thoughts, information, and previous decisions necessary to make

new decisions move through the medium of the values ether and help determine

economic decisions.

The ongoing conditioning of the ether and the statements, thoughts, information,

and previous decisions moving through it causes learning to take place and economic

decisions to change over time. It also causes individuals, businesses, and societies to react

differently to different economic stimuli even when they have the same information,

especially over time. Mikhalevskii (1971) sees the future of economic analysis being

based on continuous learning: “…the entire mechanism of economic decisions must be

based on a heurorhythmic procedure” (p.20).

Relationships and cooperation. Pressman (2005) discusses the role of Pareto

Optimality in modern economic thought that goes hand in hand with the assumptions of

individual self-interest and rationality. For an outcome to be Pareto Optimal, no one can


be made better off without sacrificing the well-being of at least one person. As a result,

the situation cannot “unambiguously be improved upon, since one person’s gain will be

another person’s loss” (p. 487). Yet, neoclassical economics, measured at the macro

level, does not allow us to compare individual gains and losses (Pressman, 2005).

Are relationships, cooperation, and rationality able to coexist? Pressman states:

“…rationality has a social dimension to it; what is rational in a situation depends not just

on what I do or choose, but also on how others react to me and to my choices” (p.490).

This concept of relationships is consistent with Einstein’s statement that “…the state of

the ether is at every place determined by connections with the matter and the state of the

ether in neighbouring places…” (1920, p. 5).

An understanding of relationships in economic decision-making helps place

individual decisions in context. For example, is an individual more likely to make an

unethical economic decision such as cheating on their income taxes if their superior at

work has encouraged them to do so? Sen (1994) discusses how social norms can have an

impact on decision-making. He uses the examples of not eating the last apple, or

automatically grabbing the largest slice of cake. Also, the decisions of individuals living

in societies with collectivist norms will be very different from those living in societies

with individualist norms.

Another motivation for decision-making explored by Sen (1994) includes the

consideration of the consequences of individual actions on others. Will someone else be

harmed or will someone be disappointed in the decision-maker due to the decision? Is

the decision-maker trying to imitate the behavior of others? What are the decision-makers

incentives within the different groups they identify with (Sen, 2004)? These relationship


considerations exist within the values ether and shape economic decisions. At the same

time, economic decisions shape relationships.

The game theory classic, the Prisoner’s Dilemma, demonstrates that the optimal

solution can sometimes be gained through cooperation. While the game does not

eliminate the possibility that individuals are self-interested, as the cooperative solution

also optimizes each individual’s benefit. It does, however, demonstrate that the

consideration of others should (and does) exist in individual economic decision-making.

The previous discussion regarding relationships also implies that there are occasions

when actors might not act in their own best interest in order to protect the interests of

others. Once again, this does not necessarily mean that individuals are not self-interested

(as an individual might gain more utility from helping someone else than from satisfying

their own immediate need), it simply means there are considerations in economic

decision-making that involve other people. Regional and international trade networks are

examples of cooperation that have economic benefits at a macroeconomic level as they

can increase resistance to recessionary shocks (He & Deem, 2010).

Sen (1994) suggests that due to social dependence, each member of a group

considers not only their independent self-interest, but also treats the joint strategy as one

of their options. In some cases this might even lead to individuals within the group being

less well off then others when cohesive actions are more desired. In his example he cites

gender-unequal societies in which women themselves might give a higher priority to the

interests and well being of the joint family unit while perpetuating their own inequality

and lower status.


Pressman (2005) concludes that the fact/value dichotomy and notion of self-

interested rationality trivializes values because relationships and cooperation with other

people creates a “social surplus.” By including other elements of behavior to the

observation of microeconomic behavior something much greater than a simple

aggregation of Pareto Optimality of microeconomic level data occurs. Mikhalevskii

(1971) states:

Even in the area of the purely economic system of values, goals, and norms,

not only is the law of superadditivity justified, but each given goal at the

macroeconomic level is qualitatively different from the corresponding

microeconomic values. (p. 19)

Sen (1994) concludes that including “other-regarding concerns in the formulation of

rational choice” will provide “better description and greater explanatory and predictive

power” (p.389).

Relativity. Another implication of Einstein’s statement regarding the state of the

ether being “at every place determined by connections with the matter and that state of

the ether in neighbouring places…” (1920, p. 5) is the concept (and theory) of relativity.

This relativity takes two forms in economic decision-making: 1) Decisions (including

values) relative to the values of others; and 2) The observed meaning of decisions

(including values) by others. While some might argue that the values ether represents

values (or moral) relativism, this author argues that it is simply a descriptive relativism

that recognizes that people disagree about the right or wrong course of action to be taken

under similar circumstances when presented with the same facts. Values, experience and


learning, perceptions of justice, relationships, and joint cooperation all play a part in this

relativity.

Ganley (1995) remarks that Veblen also recognized the concept that “conceptual

meaning was that of the observer” (p. 403). Often an individual’s decision will be shaped

based on how they believe another person will perceive their decision. For example, a job

applicant may decide not to call a perspective new employer more than once because,

while eager to have the job, they don’t want the potential employer to think they are too

assertive or “pushy”.

Actors may also compare their values to those of others when making decisions.

When we observe a co-worker demonstrating generosity with a substantial donation to

the office charity campaign, we might wish to appear equally (or even more) generous

when we make our donation. As statements, thoughts, and information move through the

values ether, decisions are formed creating new statements, thoughts, and information

that are all relative to one another.

Classifications of Statements, Thoughts, and Information

The mid-nineteenth century language of science that continues to dominate

economics requires consideration of “matters of fact” exclusively. However, the “new

science” recognizes the need to discuss possibilities and probabilities in a world that is

creative, dynamic, and engaged in continuous change while maintaining order. Revisiting

the pioneers of the science of economics, including Quesnay, Smith, and Marshall, finds

their approaches to be more holistic and consistent with the new science. They saw the

positive and normative (fact/value) as interdependent, micro and macro-level economics


coexisting, and economic systems as evolving and changing over time. They went

beyond statements of fact to explore signals of direction. Like the physical sciences in

which all physical laws continue to be obeyed, new economic systems need to be

complex systems that are “self-organized structures that absorb and dissipate energy”

while at the same time obeying some “simple behavioural rules in time and space”

(Foster, 2005, p. 1).

Based on the assumptions of the pioneers of the science of economics, it is

possible to classify the statements, thoughts, and information formed at the atomic or

macro level that move through the ether into four broad categories: 1) Facts influenced by

values; 2) Values influenced by facts; 3) Distinctive integration; 4) Full integration.

Facts Influenced by Values

An economist gives a statement of fact: “The unemployment rate is 9.6%.” This

statement is influenced by values because society has decided what facts are important to

measure and report. Schmalensee (1991) states: “Economic research, like research in any

scientific discipline, is driven in large part by an agenda that reflects the profession’s

shared sense of what problems are tractable and interesting at the time” (p. 115). The

statements of fact move through the values ether.

Values Influenced by Facts

Society identifies a problem: “The unemployment rate is too high.” This value

statement is influenced by facts because experience has shown that high unemployment

has negative consequences on both individuals and societies. The value statements move

through the values ether.


Distinctive Integration

These types of statements, thoughts, and information use both fact and value

statements, but distinguish between the two. Value statement: “The unemployment rate

should be reduced to 3.5% for full-employment to be obtained.” Fact statement: “In the

past we have tried the following solutions with the following results.” These combined,

yet distinctive fact and value statements move through the ether.

Full Integration

This category is where most decisions and actions occur. Legislators use the art of

economics to prescribe solutions to lower the unemployment rate based on an integration

of facts and values: “To lower unemployment we will increase government spending.”

The policies move through the ether and stimulate more facts based on values, values

based on facts, and distinctive integration of the two leading to more decisions using full

integration.

Figure 1 on the following page represents a potential ‘New Science’ Value-

Inclusive Model of Economic Decision-Making that positions the four classifications of

statements, thoughts, and information within the values ether. The arrows indicate that

they move through the values ether both influencing and being influenced by each other

and the collective values ether.

Role of Relationships, Cooperation, and the Invisible Hand

Throughout time the four types of statements move through the values ether,

conditioning decisions and being conditioned by them. Over time, the values in the ether

evolve and some are lost. Within the ether resides the gravitational pull of relationships


and cooperation that serves as the force that keeps us interconnected and dependent in our

decision-making while shaping the values ether and being shaped by it. On the edge of

this “Economic Universe” is the invisible hand that pushes and shapes the ever-

expanding universe while containing it within a framework of self-organization. The

model is both descriptive and predictive of a menu of outcomes.

Figure 2.1. Bell’s ‘New Science’ Value-Inclusive Model of Economic Decision-Making

The incorporation of facts and values (including their distinction and integration) as well

as relationships, cooperation, and the invisible hand results in what Sen (1994) describes

as “menu-dependent” outcomes that go beyond rational self-interest (utility).

The payoff function in a menu-dependent system does not only include the actual

outcomes that emerge, but also the set of alternative outcomes (“the opportunity set” or

Values Ether

Full Integration

Facts Influenced By Values



Relationships & Cooperation

The Invisible Hand

The Invisible Hand

The Invisible Hand

The Invisible Hand

Values Ether


“the menu”) that the individual determines they can choose by executing different

strategies. This menu of outcomes (or signals of direction) includes considerations of

how others will behave thereby creating multiple strategies. The actions of others [and

presumably value and cooperation considerations] may reduce the opportunity or menu

set over time moving the individual to a decision (Sen, 1994, p. 385). The invisible hand

can also play a part by changing overall environmental factors that can impact decision-

making (See Figure 1, above).

Final Thoughts On The ‘New Science’ Model

The mid-nineteenth century language of “facts” within the scientific

discipline moved the science of economics to a mechanical-mathematical method of

economic analysis based on the assumption of individual rational actors in

economic decision-making. The classical scientific language of “facts” and

rationality required the removal of the consideration of values, relationships, and

cooperation in decision-making. Consideration of the “unseen” was believed to be

irrational. A forced separation of positive from normative statements was also

required resulting in an assumption that what “ought” to be could not be derived

from what “is.” As the assumption of rational actors became problematic, Keynes’

theories led to the separation of micro and macroeconomics with measurement of

economic behavior focused at the aggregate level. This approach minimized the

impact of individual actors, while maintaining the assumption of rationality within

the system.

The early twentieth-century brought changes in the language of science as

scientists began exploring areas that no longer fit nicely within the confines of the


fact based, classical language of science. The science of economics, however, has

been slow to move toward a more dynamic approach to decision-making. The

model proposed in this paper attempts to return the description of economic

decision-making to a holistic approach incorporating facts and values, the positive

and normative, relationships and cooperation, and the micro and macro level

consistent with the pioneers of economic thought. At the same time the proposed

model attempts to incorporate the language and concepts associated with the

complex systems approach of the “new science.”

While the model is descriptive in nature, it has the predicative potential to

establish a menu of alternative outcomes (the opportunity set) based on a perceived set of

strategies (or signals of direction). The strategies (and the corresponding alternative

outcomes) will become increasingly limited over time based on the actions of others. The

proposed model is applicable to both micro and macro level decisions and incorporates

the dynamic nature of decision-making influenced by facts, values, relationships,

cooperation, and learning as well as their relativity to one another2.

Alternative Models and Theories of Value-Inclusive Economic Decision-Making

Suramaniam’s Fact/Value Distinction

While few models exist that attempt to integrate facts and values in a universal

decision-making model, Subramaniam (1963) does provide on alternative. In an effort to

avoid the fact/value dichotomy, his model includes values but does incorporate a

fact/value distinction. His purpose for establishing a distinction is to retain the concept of

2 Note: This is the end of the previously published work.


rationality. In fact, he suggests incorporating values makes his model an example of a

“perfect rational decision” (p. 236).

In Subramaniam’s (1963) model (See Figure 2.2, following page) individuals

begin by recalling all primary values that are relevant to the situation. The next step is to

gather all relevant facts.

Figure 2.2. Subramaniam’s (1963) Diagram of a Model of a Perfect Rational Decision

Primary Value—X Primary Value—Y Primary Value—Z

+ + +

Relevant Facts Relevant Facts Relevant Facts

Derived Value Derived Value Derived Value

Grade 1 = End Grade 1 = End Grade 1 = End

+ + +


Derived Value Derived Value Derived Value

Grade 2 Grade 2 Grade 2

= means in re: = means in re: = means in re:

Derivative Value Derivative Value Derivative Value

Grade 1 and End Grade 1 and End Grade 1 and End

in re: Derivative in re: Derivative in re: Derivative

Value Grade 3 Value Grade 3 Value Grade 3

+ + +


ALTERNATIVES Derivative Value Derivative Value Derivative Value

Grade 3 = means in re: Grade 3 = means in re: Grade 3 = means in re:


Grade 2 and Grade 2 and Grade 2 and

End in re: End in re: End in re:


Grade 4 Grade 4 Grade 4

Moment of Choice ---------------------------------------------------------------------------------------------

--

+ +

Future Relevant Facts Relevant Facts Eliminated as

irrelevant

Derivative Value Derivative Value

Grade 4 = Grade 4 =

Consequences Consequences


This process of alternating between gathering relevant values and relevant facts continues

until the moment of choice. The consequence of the choice moves into the future and

informs relevant facts and values in the next decision, unless the choice is determined

irrelevant for future decisions. While this model is a viable alternative, the constant

distinction of facts and values has not held up over time.

As an example we have seen how marketing, in its evolution to the relationship

era, has started to incorporate facts, values, and emotions into consumer decision-making.

(Bell, 2011a; Kotler, Kartajaya, & Setiawan, 2010; Peter & Olson, 2010). We’ve learned

that multiple decision-making factors can exist either sequentially (distinctive) or

simultaneously (integrated) (Bell, 2011a; Carrera & Oceja, 2007; Taylor, 2009).

Microeconomic consumer decisions are not made based on the distinction of facts and

values resulting in rational choices made by rational actors, but on the integration of

multiple cues sometimes resulting in less than rational behavior in the classic sense.

While distinction can exist at times it is integration that leads to decisions (Bell, 2011a;

Ramanathan & Shiv, 2001).

The Moral/Non-Moral Normative Judgment Distinction

Huei-Chun Su (2010) explores the dichotomous distinction of positive and

normative economics as discussed by David Colander (2001), John Stuart Mill (1963),

and British economist (and father of John Maynard Keynes) John Neville Keynes (1917).

There is a place for a positive/normative distinction as described by Colander, however,

if the analysis of economic issues resulting in applied policies is to be complete, the art of

economics integrating the positive and normative approaches must be included.

Su (2010) compares Mill’s proposed distinction between science and art, Keynes’


insistence on the use of a distinct positive science in economic decision making,

Friedman’s (1966) concerns that objectivity through positive economics alone is difficult

as economics studies the “interrelations of human beings” (Friedman, 1966, p. 4), and

Colander’s three-fold approach that suggests that “when conducting applied policy

analysis, factors which are ruled out in positive economic analysis have to be added back

in. These include non-economic factors and the operational details of institutions” (Su,

2010, p. 2). In comparing the approaches she is looking for common ground and a way to

bridge the gaps. One troubling area she seeks to resolve is Colander’s assertion that

normative judgments are value judgments, which doesn’t allow for the removal of

subjectivity insisted on by the other scholars. Even Friedman (1966), who has concerns

about objectivity, only sought to revise positive economics to eliminate “fundamental

differences in basic values, differences about which men can ultimately fight” (p. 5).

To attempt to solve this problem Su (2010) proposes the integration of the

positive and normative, but with a distinction between moral normative judgments and

non-moral normative judgments with the former occurring rarely. While she puts this

forth as her proposal to solve the conflicts between the philosophies, the original concept

of a moral/non-moral distinction in normative judgment comes from Frankena (1973).

She borrows from Frankena (1963) when she defines moral values as “not about actions

or kinds of action, but about persons, motives, intentions, traits of character, and the like,

and we say of them that they are morally good, bad, virtuous, vicious…and so on”

(Frankena, 1963, p. 8; Su, 2010, p. 24). She describes non-moral judgments as

“predominantly determined by judgments based on the knowledge of facts and scientific

theories, not moral values or rules” (Su, 2010, p. 24). Table 2.1 on the following page


from Frankena (1973) provides examples of the distinction he has prescribed. While Su

(2010) and Frankena’s (1973) normative judgment distinction has merit, the theory still

seeks to minimize the impact of values on the analysis and decision-making process.

The Normative-Affective Approach

Amitai Etzioni (1988) proposes not only an integration of normative-affective

factors (emotions and values) into neoclassical economic models, but that often logical-

empirical factors (the basis of rational decision-making) are not used at all in economic

decision-making. One problem the author notes with observing and modeling the

prominence of emotions and values in decision-making is that once the decisions are

made “Normative-affective (N/A) factors are subject to logical-empirical (L/E) research

by observers, but those actors who make them draw on value-commitments and

emotional involvements, not information or reason” (p. 126). As a result the distinct

combination of N/A and L/E is not adequately measured or known.

Table 2.1

Adapted from William K. Frankena’s Kinds of Normative Judgments from Ethics (1973)

Kinds of Normative Judgments Ethical or moral judgments proper: Nonmoral normative judgments:

Judgments of moral obligation

(deontic judgments):

Judgments of moral value

(aretaic judgments):

Judgments of nonmoral value: Judgments of nonmoral

obligation:

Particular, e.g.

(assuming

terms are used

in their moral

senses),

a. I ought not to

escape from

prison now.

b. You should

become a

missionary.

c. What he did

was wrong.

General, e.g.,

a. We ought to

keep our

agreements.

b. Love is the

fulfillment of

the moral law.

c. All men have

a right to

freedom.

Particular, e.g.,

a. My

grandfather was

a good man.

b. Xavier was a

saint.

c. He is

responsible for

what he did.

d. You deserve

to be punished.

e. Her character

is admirable.

f. His motive

was good.

General, e.g.,

a. Benevolence is a

virtue.

b. Jealousy is an

ignoble motive.

c. The man who can

forgive such

carelessness is a saint.

d. The good man does

not cheat or steal.

Particular, e.g.,

a. That is a good

car.

b. Miniver

Cheevy did not

have a very good

life.

General, e.g.,

a. Pleasure is good in

itself.

b. Democracy is the

best form of

government.

Particular, e.g.,

a. You ought

to buy a new

suit.

b. You just

have to go to

that concert.

General, e.g.,

a. In building

a bookcase

one should use

nails, not

Scotch tape.

b. The right

thing to do on

fourth down

with thirteen

yards to go is

to punt.


While economists might be concerned this approach leads to ‘irrationality’ Etzioni and

others warn not to confuse ‘irrationality’ with ‘nonrationality’. As Gigerenzer (2001)

states, “The term ‘nonrational’ denotes a heterogeneous class of theories of decision-

making designed to overcome problems with traditional ‘rational’ theories” (p. 3304).

Yet, the decisions that are made remain completely rational for the individual decision-

maker based on their decision criteria regardless of whether they are the traditional utility

maximizing, cost/benefit factors or emotional/value factors. Etzioni argues the

normative-affective approach is nonrational, while demonstrating rational characteristics

at the same time simply through a different process of decision-making.

The author believes N/A factors serve as the baseline for decision-making and

should therefore be the primary level of analysis with L/E factors added in later. Etzioni

(1988) asserts:

…normative-affective factors shape to a significant extent decision-

making, to the extent it takes place, the information gathered, the ways it

is processed, the inferences that are drawn, the opinions that are being

considered, and those that are finally chosen. (p. 127)

The purpose of Etzioni’s model is to stay closer to the neoclassical model

of economic decision-making than models of moral decision-making proposed by

others (see Latane & Darley, 1970; Schwartz, 1970; Simmons, Klein & Simmons,

1977). All of these theories assume that other emotional and value factors

influence decision making, but Etzioni’s approach shares characteristics of

Aristotle’s concept of hierarchy (Yuengert, 2000) with the N/A coming before the

L/E. This differs from Bell (2011b) who allows the N/A and L/E to be present at


the same time or hierarchically.

In Etzioni’s (1988) model the N/A factors influence decision-making by

excluding options that never make it to L/E analysis (i.e. we don’t consider

murdering our competition) or often excluding L/E considerations completely (i.e.

we don’t go beyond N/A when deciding to donate a kidney to a family member).

N/A factors also “load” facts with “interpretation, and inferences drawn with

nonlogical and nonempirical ‘weights’…that ranks options in ways that differ

from their L/E standing” (p.132). Anyone who has been “in love” has probably

observed this in his or her own decision-making.

Intrusion is another factor that often gives N/A factors prominence over

L/E factors in Etzioni’s model. He states that “…L/E considerations require

completing a sequence that involves collecting facts, interpreting their meanings,

and drawing inferences leading one to favor one option over others” [emphasis in

the original] (1988, p. 132). However, N/A factors can interrupt the L/E

considerations causing individuals to skip steps, under analyze information, or

inadequately complete them. Completing the sequence of L/E considerations

requires a great deal of discipline, self-awareness, and a high level of attention

and concentration. Similar to Simon’s (1991) concept of bounded rationality, the

limitations of our cognitive processes cause us to take shortcuts or get tunnel

vision leading to N/A factors taking over. This is consistent with the bounded

rationality problems discussed by other scholars concerned with the behavioral

aspects of decision-making (see Kahneman, 2003; Kahneman & Tversky, 1982;

Posavac, Kardes, & Brakus, 2010; Thaler & Sunstein, 2009). Etzioni (1988)


argues this occurs because “L/E thinking is conducted ‘vertically’, in sequences,

[and] N/A ‘considerations’ [use] ‘lateral thinking’” (p.133).

In short, Etzioni (1988) does not believe rationality is short-circuited in

this N/A hierarchical model; it simply helps in the decision-making process. To

put it in the language of Sen (1994) and Bell (2011b) N/A factors help establish a

menu of choices by excluding options. These choices are then analyzed using L/E

factors and a decision is made. Sometimes there can be negative consequences

(i.e. tunnel vision or ineffective shortcuts), but overall serves as a positive method

of meeting the multiple ‘nonrational’ needs of individual decision makers.

Specifically the author views Pieters and Van Raaij’s (1987) four functions of

affect as having positive impacts on decision-making. They include the

interpretation and organization of information, the mobilization and allocation of

resources, sensation seeking (when bored) and avoiding (when stressed), and by

providing a means of communicating feelings and preferences.

Behavioral Economics/Finance

The field of behavioral economics/finance is one area of decision-making that

deserves consideration in the discussion of ‘nonrational’ economic decision-making

models. This field deals with issues like biases (actor/observer, status quo, confirmation,

not invented here, availability, and short-term), and individuals’ propensity to engage in

herding behavior, use heuristics or rules of thumb, be overconfident, and a tendency to be

loss averse (Thaler & Sunstein, 2009). The authors suggest these biases can sometimes be

overcome by choice architecture or “nudges” that move people toward the decision that is

in their (rational) best interest. For example, to get people to save for retirement,


employers can set up defined contribution plans so that individuals must opt-out rather

than opt-in to the plan as status quo biases lead people to keep the default option. Thaler

(1988) has also explored the behavioral aspects of cooperation (as has Sen, 1977; 1994)

in economic decision-making.

Becker (1968) has also done some interesting work in behavioral economics. He

treated crime and punishment as a constrained optimization problem to minimize the

social costs of crime. He found the optimal solution was to minimize monitoring or

surveillance and maximize fines. How punishment structures are designed impacts

decisions about the allocation of time between crime and legitimate employment. In other

words, creating behavioral structures in which “crime does not pay” seems to have the

greatest social benefit. This includes higher fines or greater punishment for crimes with

lower elasticities.

While Simon’s (1991) bounded rationality has previously been mentioned,

Kahneman and Tversky’s (1979) prospect theory uses cognitive psychology to explain

why rational decision-making (specifically expected utility theory) is not representative

of how individuals make decisions under risk. Like Etzioni (1988), prospect theory is

hierarchical in nature with people first editing and then evaluating based on utility.

During the editing process individuals use a variety of means to “rank” the prospective

decisions leaving them with a smaller set from which to choose. Unlike Etzioni, prospect

theory does not necessarily insist emotions and values drive the editing process, although

they may be a part of it. The theory also asserts that individuals are more likely to assign

utility value based on loss aversion and relative gains. In other words, they try to

minimize losses rather than maximize gains. Their decisions are also considered subject


to intransitivity as they are based on the relative preferences between pairs of options.

This is different from the expected utility theory of rational decision-making models in

which absolute wealth (or gain maximization) is the most important factor and it is

assumed that all information is analyzed to reach the rational decision. Dan Ariely (2008)

provides evidence in his book Predictably Irrational that supports many of the behavioral

aspects discussed here. These include an individual’s propensity to regard their

environment and decisions in terms of their relation to others, asymmetric dominance

effects, anchoring, ignoring opportunity costs, and the role of social norms, emotions,

expectations, and self-control in decision-making.

Neuroeconomics

The emerging field of neuroeconomics is attempting to determine all the different

ways the brain drives decision-making. Neuroeconomics is combining behavioral

economics with neuroscience, cognitive and social psychology, and other experimental

methods to understand more about how individuals make economic decisions and how

they inform the other decisions in our lives. The field seeks to determine what variables

the brain computes to make economic decisions, how the underlying neurobiology helps

and constrains decision-making, and how it improves the understanding of behavior and

well-being in economic, political, clinical, legal, and business contexts among others

(Dayan, 2008; Fehr & Rangel, 2011).

Naturalistic Decision-Making

Another emerging field of decision-making is Naturalistic Decision-Making

(NDM). NDM attempts to determine how people use their expertise and other factors in


actual decision-making as opposed to in a laboratory environment. It is primarily

concerned with proficient decision makers with relevant experience or knowledge in the

area in which they are making decisions. The need for “expertise” and observations in

actual decision-making situations makes this method of inquiry unique. However, while

the method is broad in its approach to factors leading to decisions, the need for expertise

makes it a more narrowly focused inquiry as individuals often make decisions in areas in

which they are not experts. In these cases the decision-making process might be quite

different (Lipshitz et al., 2001).

The method is relevant to this discussion because the characteristics of decision-

making explored go beyond traditional rational methods. One characteristic is

considering the cognitive processes of proficient decision makers. NDM also considers

how well experts match decision actions with situations. For example, experts often

quickly screen out most options by comparing them against a standard for the situation

rather than compared to one another. This includes an analysis of an option’s

compatibility with a decision-makers values, rather than just its relative merits (Beach,

1990; Lipshitz et al., 2001). Unlike some other approaches, NDM also believes “that

‘ought’ cannot be divorced from ‘is’: prescriptions which are optimal in some formal

sense but which cannot be implemented are worthless” (Lipshitz, Klein, Orasanu, &

Salas, 2001, p.335). In other words, practical application is necessary to justify decision-

making theory.

Other ‘Nonrational’ Theories and Models

The purpose of this portion of the literature review was to discuss the nonrational

theories that attempt to overcome some of problems associated with assumptions of


rationality in economic (macro and micro) decision-making models. As the scope of this

research does not allow for an in-depth discussion of all scholarly thought on nonrational

theories, some of the theories/models that most closely align with existing classical,

neoclassical, and contemporary economic thought have been highlighted. The goal is to

add layers of analysis on top of rational decision-making models and consider options

that will enhance existing theory.

A second goal is to provide evidence of scholarly thought on the role of decision

factors like values, emotions, learning, concern for others, culture, and the impact of

individuals in economic decision-making. With that in mind, there are other decision-

making theories that have not been discussed in detail here, but may be drawn from in

later analysis. Aspects of many of these theories can be seen in several of the models and

theories already discussed.

Final Thoughts On Value Inclusive Economic Decision-Making Models

While Bell’s (2011b) model draws heavily from the classical economic literature

and even physics, the fields of ethics, psychology, behavioral economics/finance,

neuroeconomics, and naturalistic decision-making all offer insights into the ‘nonrational’

models of economic decision-making. We see recurring themes of relativity, complex

systems, concern for others, bounded rationality, behavioral factors, natural or organic

models, and a lack of dichotomy between the positive/normative and fact/value (although

distinction remains applicable at times).

Assuming a role for values in economic decision-making, the following section

explores two questions:


1) How are individual values that go into our microeconomic decision-making

formed?

2) In what ways do we apply those values in microeconomic organizational

decisions?

Value-Inclusive Microeconomic Decision-Making

If we are going to understand the role of values in microeconomic decision-

making, it will be necessary to explore how values are developed and how they might be

relevant in the business environment. In this section some notable theories associated

with the development of individual values will be discussed. In addition, studies that

indicate how personal values impact organizational decision-making at the

microeconomic level will be considered in the final portion of the literature review.

Individual Value Development

To be inclusive of values in economic decision-making, it will be necessary to

define individual values and how they are formed. Rokeach (1968) defines values, “as

abstract ideals, positive or negative, not tied to any specific object or situation,

representing a person’s beliefs about modes of conduct…” (p. 124). He believes these

values guide individual actions. Personal values are those values that an individual is

committed to and that influence behavior and guide the actions of the individual (Kaushal

& Janjhua, 2011; Theordoron & Achilles, 1969). Consistent with Bell’s (2011b),

Etzioni’s (1988), Huei-Chun Su’s (2010), and Subramaniam’s (1963), and models

described above, Shalom H. Schwartz (1992) views values as criteria in decision-making


(to both select and justify actions) and not just descriptions inherent in individuals.

People use values to “select and justify actions and to evaluate people (including the self)

and events” (Schwartz, 1992, p.1). He has also demonstrated that values can be impacted

by social structure like education, age, gender and occupation as well as unique life

experiences. Personal values influence individual’s ideologies, attitudes, and actions

across cultures.

Kohlberg and Value Development

Lawrence Kohlberg has conducted extensive research on whether the moral

values of individuals have the ability to develop over time. Based on his research, he

developed what he describes as “a culturally universal invariant sequence of stages of

moral development” (Kohlberg, 1973, p. 630). His model consists of six stages within

three levels of development

The following is adapted from Kohlberg’s (1973) description of the model:

I. Preconventional level

At this level an individual is concerned with cultural rules, good and bad, right or

wrong. Individuals associate these rules and labels in terms of consequences like

punishment, reward, or exchange of favor.

Stage 1 is a punishment-and-obedience orientation. Individuals are not concerned with

their own meaning of value, but obey to avoid unpleasant consequences.

Stage 2 is the instrumental-relativist orientation. In this stage individuals take actions

that satisfy their own needs while occasionally satisfying the needs of others.

Reciprocity is simply a way to ensure you can continue to get what you want. The

concept of fairness comes into play, but only in a very pragmatic way. For example, a


child might want to make sure his sister is not getting a bigger piece of cake than he is.

II. Conventional Level

At this level individuals become concerned with maintaining the expectations of their

family, group, or nation. These expectations are considered valuable on their own and

are considered by choice rather than out of concern for consequences. Individuals

desire to maintain, support, and justify the order of the group. The do not desire to

simply conform, but wish to identify with the group.

Stage 3 is referred to as the interpersonal concordance or “good boy—nice girl”

orientation. Individuals become aware that good behavior is what pleases others and is

approved by them. Individuals will try to conform to their perceived stereotypes of the

group. You earn approval by being nice and behavior is often judged by intention.

Stage 4 is the “law and order” stage. Maintaining the social order through authority and

fixed rules is important. The correct way to maintain social order in this stage is

through doing one’s duty and showing respect for authority for its own sake.

III. Postconventional, autonomous, or principled level

At this level individuals have formed their own moral values and principles. These

values and principles are not a part of the individual’s identification with the group, but

stand on their own validity.

Stage 5 is the social-contract legalistic orientation. This orientation generally comes

with utilitarian overtones. Right actions are framed within the context of individual

rights and standards that have been critically examined and agreed upon by the society

as a whole. The relativism of personal values and opinions is recognized and

procedures for reaching consensus are established. The American government and


constitution are based on this form of morality as rational considerations of social

utility are applied that not only emphasize the legal point of view, but also the

possibility of changing the law. This is different from Stage 4 where the rules are fixed.

Stage 6 is the universal-ethical-principle orientation. At this stage universal principles

of justice, the reciprocity and equality of human rights, and respect for the dignity of

people as individuals become guiding, self-chosen principles. The rules that guide the

individual are abstract and ethical, and not absolute.

Kohlberg (1973) argues that not only does our morality develop over time, but our

logic does as well. He describes this as a transformational process that makes us better

decision-makers over time. The theory shares the hierarchical characteristics of the

models of decision-making described earlier (Rest, 1973). His final stage is closely

aligned with the theory of justice as described by John Rawls (1971). Kohlberg’s model

seeks to use an interdisciplinary approach that fuses the theories of moral philosophy set

forth by Rawls and Jean Piaget (Rest et al., 2000). It combines the inherent morality and

justice seeking of Rawls (1971) with the genetic epistemology approach of Piaget that

assumes the development of moral judgment with age (Piaget, 1932/1997).

The Neo-Kohlbergian Perspective

While some scholars agree with Kohlberg’s approach, there were many who do

not. As a result, a Neo-Kohlbergian perspective emerged. Bebeau, Narvaez, Rest, &

Thoma (1999) believed Kohlberg’s theory had merit, but required some modifications.

They believed several of Kohlberg’s core ideas required revisiting; specifically his

emphasis on cognition, individuals self-constructing categories of morality (like justice,


duty, rights and social order), that one set of moral concepts was more developed and

“better” than others, and the “macromoral” focus on the formal structures of society in

the form of laws, roles, institutions, and general practices.

The authors first propose a distinction between and recognition of macromorality

(as described above) and micromorality that deals with personal interactions, how we

treat people, and “generally acting in a decent, responsible, empathic way in one’s daily

dealings with others” (Bebeau, et al., 1999, p. 2). They further describe the distinction

between macromorality and micromorality as follows:

In micromoral issues, what is praiseworthy is characterized in terms of

unswerving loyalty, dedication, and partisan caring to special others…in

macormorality, the praiseworthy response is characterized in terms of

impartiality and acting on principle, instead of partisanship, favoritism, or

tribalism. Both macro- and micromorality concern ways of constructing

and enriching the web of relationship’s—one through the structures of

society, and the other through personal face-to-face relationships. (p. 3)

While Bebeau, et al. (1999) see room for both micro- and macromorality to

coexist, they believe both Kohlberg and the Neo-Kohlbergian approach are better at

measuring the macromoral. This is consistent with J. N. Keynes (1917) and J. M. Keynes

(1936) need to isolate macro factors from micro factors for ease of analysis in economic

decision-making. This is a limitation of both approaches because it requires a high level

of abstract thinking that doesn’t apply well to individual decisions. Most individuals

don’t engage in abstract, philosophical thought prior to making a decision. Factors like

personal and immediate group values, relationships, emotions, behavioral, and other


‘non-rational’ factors as described in the economic decision-making models in this paper

demonstrate the more unconscious and automatic processes individuals utilize.

Naturalistic decision-making research supports Bebeau’s assertions as it has finds the

complexity of the decision-making process under uncertainty causes us to match

responses to cues from the situation or environment that often rely on informal reasoning.

Neo-Kohlbergians also recognize the potential for conflict between the micro- and

macromoral considerations and therefore justify the macromoral limitation as preferable

because the ultimate goal should be for the betterment of society through impartiality,

fairness, and justice. As a result the Neo-Kohlbergian approach carries forward the

Rawlian characteristics of the original theory. Bell’s (2011b) approach to economic

decision-making attempts to combine the micro and macro into one model that

incorporates both. In her model the values ether both shapes and is shaped by both micro-

and macro-level values with the superadditivity of micro-level values influencing and

being influenced by the macro-level.

The Neo-Kohlbergian perspective also argues against a stair-step approach to

moral development as proposed by Kohlberg (1971) and Piaget (1932/1997). Instead they

argue for gradual shifting toward the use of higher forms of thinking that are not tied in to

age or hard stages of development. They also reject the notion that advanced moral

thinking is the exclusive result of individual cognitions uninfluenced by other people and

society. However, it does retain the assumption of rationality (Narvaez, 2005). Bell’s

(2011b) model is consistent with this approach as it attempts to incorporate factors like

relationships and cooperation as presented by Sen (1994; 2004) as well as society, but

does remove the assumption of rationality in its classic definition of purely self-interested


utility maximization based on cost/benefit analysis. Instead rationality includes multiple

interests including commitment that make multiple decisions equally rational, but for

different reasons for different people (Bell, 2011b; Sen, 2005).

To replace stage theory, Bebeau, et al. (1999) use schema theory to determine

moral development. They use the Defining Issues Test (DIT) to activate and assess moral

judgment. The purpose is to determine which schemas an individual brings to a task

when making a decision. While they do not fit neatly into “stages” they do determine

what set of schemas an individual is using in decision-making. This set of schemas has

proven effective in determining the level of moral thinking an individual is using

(Narvaez, 2005; Rest et al., 2000). These schemas already exist in the individual’s head

or long term memory and are presumed to structure and guide people’s moral thinking

(Rest, Narvaez, Bebeau, & Thoma, 1999). This shift from stage theory to schema theory

is the most significant contribution of the Neo-Kohlbergian school and attempts to bridge

the gap between cognitive science and moral psychology. It also seeks to move the theory

beyond the exclusive moral judgment approach of Kohlberg and incorporate moral

sensitivity, moral motivation, and moral action (Narvaez, 2005).

Social Learning Theory

The concept of social learning theory could easily have been included in the

decision-making models and theories portion of this paper as well as this section on how

individual values are formed. I have chosen to put it here as Bandura (Bandura, 1977;

Wood & Bandura, 1989) first considers how values are formed and then how they are

utilized. Applications of the theory will be described in more detail in the following

section. Social learning theory was developed by Albert Bandura (see Bandura, 1977) in


response to what he saw as shortcomings in Miller and Dollard’s (1941) concepts of

modeling in Social Learning and Imitation. Bandura (2007) summarizes Miller and

Dollard’s concept of modeling as follows: “A model provides a social cue, the observer

performs a matching response, and its reinforcement strengthens the tendency to behave

imitatively” (p. 55). His concern was that in real life we don’t simply mimic specific acts

or wholly incorporate the personality patterns of another person (the model). Instead, he

saw observational learning as selectively and conditionally manifesting characteristics of

the model. He saw the process of social modeling as having a cognitive component and

not simply mirroring others (Bandura, 2007). He also rejected the traditional assumptions

of behaviorists, which emphasize environmental determinism and minimize the

contributions of cognitive processes, especially cognitive mediators (Kytle, 1978). In

terms of values development, this makes it possible to cognitively reject the values of

your parents and selectively adopt the values you observe (and like) in others.

Bandura rejects the notion of human behavior being explained in terms of one-

sided determinism. He believes individuals learn within a social context, this includes

their values and beliefs about themselves and others (Bandura, 1977; Wood & Bandura,

1989). Rather than one-sided determinism, he proposes a triadic reciprocal determinist

approach for how we learn values and behaviors and achieve behavioral changes. In his

model of social learning theory, behavior, personal factors including cognition, and the

external environment “operate as interacting determinants that influence each other

bidirectionally…Because of the bidirectionality of influence, people are both product and

producers of their environments (Wood & Bandura, 1989, p. 362). This concept is similar

to Bell’s (2011b) model of the values ether in which current and past decisions, learning,


relationships, and cooperation all influence each other bidirectionally, both influencing

and being influenced by one another. Like Bell, change and context become part of the

determinants of the description and explanation of how values are formed within

Bandura’s social learning theory (Lerner, 1990).

Social learning theory is based on the premise that information is conveyed by

models through direct observation called observational learning. In order for

observational learning to occur, four processes must be present:

1) Attentional processes—Attention determines what people selectively observe and

what information they extract from the modeled activity.

2) Cognititive representational processes—This is the process of actively retaining

information by transforming the observation into rules and conceptions.

3) Behavioral production process—In this process the rules and conceptions

previously determined are translated into courses of action.

4) Motivational processes—There are three primary types of motivators:

a. Direct—The action will produce valued outcomes.

b. Vicarious—People can be motivated by the successes of others who are

similar to themselves.

c. Self-produced—People generate self-evaluations that regulate which

observationally learned actions they will choose. Values and values

formation can be a significant influence in this area (Wood & Bandura,

1989).


The social contexts and observational learning processes described by Bandera

can certainly influence individual value development. Individuals can also influence the

value of others through value modeling.

Final Thoughts on Individual Value Development

The three perspectives discussed here describing how individual values are

formed have several differences. Kohlberg’s approach is based on inherent morality and

the genetic epistemology of moral development that incorporates a significant role for

individual cognition. The Neo-Kohlbergian perspective seeks to add incorporate moral

sensitivity, moral motivation, and moral action to Kohlberg’s approach. The Neo-

Kohlbergian school also recognizes that micro-moral factors are present and may conflict

with macro-moral factors, yet continues to evaluate the issue from a macro perspective

for ease of analysis as the good of society is viewed as the primary interest. The stair-step

approach to development, not recognizing the potential impact of micro-moral issues, and

one set of moral concepts being better than another are rejected and the roles of

individual cognition and self-constructing of morality are minimized in favor of a

“schema” approach in Neo-Kolberianism. While not specifically analyzed, the influences

of other people and society are recognized (Bebeau et al., 1999; Narvaez, 2005; Rest et

al., 2000).

Bandura’s (1977; Wood & Bandura, 1989) social learning theory sees a much

more active role for social context, learning, change, and observation resulting in a

cognitive process of selectively and conditionally adopting values within the value

development process. Bandura’s approach is similar to Schwartz (1970; 1992) who


incorporates social structures like education, age, gender and occupation as well as

unique life experiences into the process of value development. These approaches are

much more holistic in their approach.

Bell’s (2011b) model more closely aligns with the approaches to value inclusive

decision-making and the formation of values suggested by Bandura (1977) and Schwartz

(1970; 1992). Bell’s model is based on the bidirectional influence of individuals and

society including concern for factors like relationships, cooperation, experience,

information, learning, complexity, and multiple equilibria. There is also a place for the

Neo-Kohlbergian (Bebeau et al., 1999, Narvaez, 2005; Rest et al, 2000) concept of

schemas in Bell’s model, but she would argue that there are both micro and macro

influences on those schemas, which may evolve over time. Overlaying the Neo-

Kohlbergian and social learning approaches of value development with Bell’s (2011b)

model would result in the following revised model in Figure 2.3on the following page.

Even with their differences, there are some core similarities between the theories

of moral development. The first similarity is that morality and values do have a role in

decision-making and they do evolve over time. A second similarity is the involvement of

others either in our concern for others in our decision-making or the influences of others.

Even Kohlberg’s approach that is based strongly on an internal cognitive approach to

development does include the influence of others. For example, even in the Level 1,

Stage 1 punishment-and-obedience orientation, someone else is determining what is right,

what is wrong, and when an individual should be punished or rewarded.


Figure 2.3. Bell’s ‘New Science’ Value-Inclusive Model of Economic Decision-Making

With Neo-Kohlbergian & Social Learning Values Development

Value-Inclusive Microeconomic Decision-Making in Organizations

If values are included in economic decision-making and individual values are

formed in a variety of ways, why should businesses and other organizations be concerned

with values? The most obvious reason is that individuals in organizations make

microeconomic decisions that can impact the entire organization. The values of

individuals in organizations can determine the values of organizations including the

ethical practices that make or break them. The ethics scandals that rocked companies like

Enron, Tyco, and WorldCom all involved the personal values of individuals within these

companies (McLean & Elkind, 2004). Behavior in an organization is a manifestation of

Values Schema Ether

Full Integration

Facts Influenced By Values



Relationships, Cooperation & Social Learning

The External Environment




Values Schema Ether


attitudes and values. If behaviors and their actions are manifestations of values, then

values are both inferred from behavior and may predict behavior in organizations

(Churchman, 1961; Connor & Becker, 1975; England, 1967).

Studies have indicated that individuals may bring the personal values they use

routinely, to their decisions at work. Personal values that closely match organizational

values result in greater organizational commitment and are linked to variables like

absenteeism, turnover, job satisfaction, organizational citizenship, and job performance

(Finegan, 2000). In addition to organizational commitment, worker satisfaction is greater

when their values are congruent with those of their immediate supervisor (Jiang, Lin, &

Lin, 2010; Meglino, Ravlin, & Adkins, 1989) . Values can also impact vocational choice

and the likelihood of rising to a leadership position (Finegan, 2000; Keltner, Langner, &

Allison, 2006; Palmer, 2000). Individual value characteristics like conscientiousness,

agreeableness, neuroticism, Machiavellianism, moral reasoning, and locus of control

along with moderating influences like the need for power and moral utilization all

influence ethical leadership (Brown & Trevino, 2006). Values are the “prime drivers of

personal, social, and professional choices” (Suar & Khuntia, 2010, p. 443).

Cultural and work contexts can also impact values and their use in business

decisions (Gamble & Gibson, 1999; Suar & Khuntia, 2010). Gamble and Gibson (1999)

found that the personal/cultural value of collectivism based on personal relationships

manifests itself at work with Chinese financial controllers. When making business

decisions, they did not attempt to meet objective performance criteria, but instead used

‘relationships’ and ‘organization’ considerations in decision-making. Cooperating with

peers and supporting supervisors had significant weight in decision-making. Work values


emerge from personal values because work values emerge from the projection of personal

values into the domain of work (Kaushal & Janjhua, 2011; Ros, Schwartz, & Surkis,

1999).

As important as value congruence is to organizational commitment and individual

performance, personal values also determine ethical practices at work. As Suar &

Khuntia (2010) state:

Irrespective of the type of organizations and age of managers, personal

values more potently and consistently decreased unethical practices and

increased work behavior compared to value congruence. Hiring managers

emphasizing personal values can demote unethical practices and promote

work behavior. (p. 443)

If we assume that Bandura (1977) is correct and individuals continue to develop

contextual values based on observational learning, organizational leaders are pivotal in

ensuring the ethical practices in business decision-making of subordinates. Leaders who

articulate their values and model ethical behavior can reduce unethical behavior and

interpersonal conflicts (Mayer, Aquino, Greenbaum, & Kuenzi, 2012), make ethical

behaviors by subordinates a habit (Almeida, 2011), positively impact the personal values

of employees (Weiss, 1978), create more value for customers through market orientation

(Lam, Kraus, & Ahearne, 2010), and demonstrate justice and care during a corporate

crisis (Simola, 2003). They help form the complex mental models that all members of the

organization use in the decision-making process (Courtney, 2001; Goel, Johnson,

Junglas, & Ives, 2010) and influence the meanings individuals give to experiences in the

sensemaking process in organizations (Weick, 1995; Weick, Sutcliffe, & Obstfeld, 2005).


Of course this is a double edged sword and leaders like John Gutfreund at

Salomon Brothers and Ken Lay and Jeff Skilling at Enron who express negative values

and model unethical behavior will most likely lead observers to either leave the company

or adopt the unethical practices embedded in the culture (Sims & Brinkmann, 2002;

McLean & Elkind, 2004). As a result, organizations have a vested interest in

understanding the role of values in organizational decision-making and how those values

and the resulting behaviors are formed.


Chapter 3

Methods

The purpose of this study was to identify the relationships between the personal

values that individuals in a group assign to decision considerations and the future

decisions of those individual group members. The role reflecting on and sharing values

plays in our development of decision-making schemas, and whether these factors can

interrupt existing, unconscious decision-making schemas will be explored. A quasi-

experimental pretest/posttest design with 5 groups was utilized. A quasi-experimental

design that includes multiple groups was selected to minimize certain internal validity

threats to single group models including history, maturation, selection, mortality, and

experimenter biases. The design of the experiment also helped minimize some of these

threats. For example, the history, maturation, and mortality threats were minimized

because the pretest, treatment, and posttest all occurred during the same class period over

approximately 1 1/2 hours.

Sample

Participants were selected using convenience sampling from 5 undergraduate

business classes with 3 at the Mat-Su College of the University of Alaska Anchorage

(MSC) and 2 at Colorado State University (CSU). The MSC groups were between 13 and

22 students and the 2 CSU groups ranged from 35 to 45 students. While convenience

sampling was utilized to choose the classes to participate, the students had randomly self-

selected to enroll in the programs and courses with no influence from the researcher.


These two campuses provide very different environments and student populations.

CSU is a large research university with approximately 23,000 students in Fort Collins,

CO (U.S. News & World Report, 2013). While not an “urban” campus the population of

the city is approximately 147,000 and the city website describes Fort Collins as “rapidly

urbanizing”. CSU is also a “selective” college (75.9% accepted) with specific and

rigorous admission requirements that must be met by all students.

MSC is a small satellite “teaching” campus of the University of Alaska

Anchorage with a 2012/2013 academic year student population of approximately 1,900.

The campus is located on the border of the towns of Palmer and Wasilla, Alaska that

have a combined population of approximately 14,214. MSC draws students from the

greater Mat-Su Borough with a population of approximately 92,000 in an area the size of

the state of West Virginia. The campus offers Associate Degrees and coursework leading

to Bachelor’s degrees at the University of Alaska Anchorage. MSC is an “open

enrollment” campus meaning there are no admission requirements other than taking the

university placement tests for math and English. Of those students enrolled in the

College, approximately 25% do not have college level math or English skills and are

required to take developmental classes.

One reason for choosing classes is that Bell’s (2011b) model and other scholars

including Bandura (1977) and Sen (1994) suggest relationships may play a role in the

formation of values. Students in the same class have had an opportunity to form

relationships and ideas about one another that would be lost if the class members had

been separated and randomly assigned to groups. While this is only one type of

relationship, it serves as a control for this study and does not necessarily limit


generalizability. Members of a class have a variety of levels of “closeness” with other

individuals in the class as we all do with the individuals we encounter on a daily basis.

This study is not designed to test level of relationship, only to choose groups in which

some relationship exists. Classes have the added benefit of being similar to work groups

in the business environment, which is the intended application of this study.

All subjects were adults and no members of vulnerable populations were utilized.

Instrumentation

Participants were given the Defining Issues Test-2 (DIT2) as the pretest and

posttest. This test was originally developed by James R. Rest (1979) and later updated

and revised in the second version used here. Scored results are based on Kohlberg’s

(1973) Stages of Moral Development, but it is designed to test the unconscious schemas

consistent with the Neo-Kohlbergian perspective. The instrument has been used in over

400 published articles (Center for the Study of Ethical Development, 2012) many times

with university students (Cesur, S. & Topcu, M. S., 2010; Lies, J. M., Bock, T.,

Brandenberger, J., & Trozzolo, T. A., 2012; Pagano & DeBono, 2011; Woodward, Davis,

& Hodis, 2007) including at least one study specifically using business students (Lan,

Gowing, McMahon, Rieger & King, 2008). The DIT has proven to be very reliable with

Cronbach’s alpha ranging from the upper 0.70s to the low 0.80s over 25 years of data.

Test-retest results without interventions have proven to be about the same (Rest et al.,

2000). An example of the pre-test and post-test instrument are in Appendices A and B of

this document.

Participants were also given a list of values and anti-values to serve as prompts.

The value prompts were adapted from Schwartz’s (1992) list of universal values and


motivational types of values. Schwartz’s values list was modified to include opposite

pairs or value/anti-value pairs (See Appendix C). For example, one of Schwartz values

was honesty; the value prompts for this study includes the matched pair of

honesty/dishonesty. The reason for this modification is that when participants are asked

to select a personal value they believe applies to a particular decision consideration, the

decision consideration might indicate a lack of the value of honesty. For example, if one

of the decision considerations participants are asked to assign a value to is: “To prevent

going to jail, I would lie,” this may indicate a lack of the honesty value. In this case the

value I might associate with this decision consideration is the anti-value of dishonesty.

Anti-values were developed from antonyms in The Merriam-Webster’s Collegiate

Thesaurus (2nd

ed.).

Demographic information was collected including age, gender, time in business

cohort, years of full-time/part-time work experience outside the university, and number

of years in a leadership position in full-time/part-time work.

Students were also asked about their perceived use of values and ethical decision-

making skills to determine their self-expectations. These questions were important

because expectations of self and others have been considered as possible motivations for

decision-making. While, this study explores whether the reflection on and sharing of

values impacts an individual’s decision-making, it is also important to capture self-

expectations about how individuals make decisions for future analysis. The two questions

that were asked included:

1. Do you consider your personal values when making work decisions?

Yes/No


2. Do you consider yourself an ethical decision maker? Yes/No

Experimental Design and Procedures.

Design. As previously discussed this quasi-experimental study will use a

pretest/posttest design with multiple groups. While participants will not be randomly

assigned to groups students have randomly self-selected to be in the class and were not

placed in groups by the researcher. The experiment also requires students to be part of a

group or community within which they may have developed relationships. Using the

standard classic notation system the quasi-experimental design is as follows:

Test Groups: O----------X----------O

Pretest. During the pretest, participants will be given the Defining Issues Test-2.

The researcher will give instructions and offer to answer any questions.

Treatment. The researcher will distribute the list of value/anti-value prompts and

give participants an opportunity to read the list and ask any clarifying questions. If

students ask questions about what a particular value means, the researcher will provide a

definition. All definitions of values will come directly from Schwartz’s (1992) definitions

included in his list on pages 61-62 of his study. Anti-value definitions will come from

Merriam-Webster’s Collegiate Dictionary, 11th

ed. (2008).

A list of the decision considerations participants were asked to rate in scenario 1

was distributed (See Appendix D). Participants were asked to assign the value they

believe most adequately reflected the personal value of an individual who would consider

that issue to be assigned “great” or “much” importance in the decision for that scenario.

Participant’s answers were collected and the researcher projected a word

document on the screen with each of the decision considerations from scenario 1. With


the group, the researcher randomly selected 5 to 13 response sheets from students and

added the values the participants assigned to each of the decision considerations, creating

a values set for each issue. No discussion was allowed, but participants were given a few

minutes to review the information.

Posttest. The participants will retake all parts of the Defining Issues Test-2.

Pilot Test

The process and instruments were piloted tested with 30 undergraduate students

in an Introduction to Business class at the University of Alaska Anchorage, Mat-Su

Campus. Students found the DIT2 easy to understand, but somewhat time consuming.

The students also found the Value Prompts understandable and relevant to the decision

considerations. They had no additional suggestions for how instructions or the

instruments could be improved.

The original method called for all students’ responses to the values expressed on

Scenario 1 to be shared with the group. I found it to be very time consuming during the

pilot and noticed students starting to lose interest after awhile. As a result, I decided to

pull a random sample of 10 to share. The pilot took approximately 1½ hours, but I

believed I could reduce the time by 15 minutes with this modification. The time reduction

proved to be true for all but 1 of the participating groups.


Data Analysis

As the DIT2 is a proprietary instrument owned by the University of Alabama,

responses were sent to UofA for scoring. The test results provided the following scores in

Table 2 on the following page. I completed statistical data analysis by group, school, and

for the data as a whole.

Table 3.1

DIT-2 Measured Scores

Stage 23 Score Personal Interest Schema Score: this score represents the proportion

of items selected that represents considerations from Stage 2 (focus on

the personal interest of the actor making the moral decisions) and Stage

3 (focus on maintaining friendships, good relationships, and approval).

Stage 4P Score Maintaining Norms Schema Score: this score represents the

proportion of items selected that represent consideration from Stage 4

(focus on maintaining the existing legal system, roles, and formal

organizational structure).

P Score Postconventional Schema Score: this score represents the proportion

of items selected that represent considerations from Stage 5 (focus on

appealing to majority while maintaining minority rights) and Stage 6

(focus on appealing to intuitive moral principles or ideals).

N2 Score New Index Score: this score represents the degree to which

Postconventional items are prioritized plus the degree to which

Personal interest items receive lower ratings than the Postconventional

items. This score is adjusted to have the same mean and standard

deviation as the P score to allow for comparisons.

U Score Utilizer Score: This score represents the degree of match between

which items the participants rated as most important and what decision

participants say they would make in the moral dilemma.

Hum/Lib Score Humanitarian/Liberalism Score: this score represents the number of

reported decisions for the moral dilemmas that match those chosen by a

group of “experts” (professionals in the field of political science and

philosophy). Scores range from 0 to 5 out of the possible 5 moral

dilemma decisions that can match.

Cancer10

Score

Religious Orthodoxy Score: this score represents the sum of the rated

importance and rank for one specific item from the Cancer moral

dilemma that evokes the notion that only God can determine whether or

not someone should live or die.


A Score Antisocial Score: this score represents the degree to which items are

selected that represents considerations that reflect an anti-establishment

attitude. These considerations presuppose Stage 4, but fault the

establishment for being inconsistent with their purpose.

M Score Meaningless Score: this score represents the degree to which the

survey results are meaningless. The higher the score the more

meaningless the individual survey results. Note. Adapted from “Defining Issues Test-2: Spring 2009,” by Texas Tech University, 2009,

Retrieved from www.depts.ttu.edu/provost/qep/docs/DIT_Spring2009.pdf

The total respondents and number of valid responses were recorded. M scores

were checked and the scorer removed participants with high M scores from the study. M

scores measure the meaningless items and are a test for response validity. Bebeau and

Thoma (2003) describe these items as:

…items that contain unusual, pretentious words or complex syntax, but the

items aren’t meaningful to the dilemma. “M” items are ‘high sounding’

but deliberately designed to be meaningless. If a subject endorses too

many of these items (greater than 10), we assume that the subject is

responding to style of wording and syntax rather than to meaning, and

therefore we invalidate the protocol. (p. 7)

To determine if a change has occurred in the level of ethical decision-making

utilizing the DIT2, the relevant scores to consider are the P Score and the N2 Score. I also

included analysis of the Utilizer score as it has relevance to this study. While not

relevant to the study, I did analyze the results from Stage 23 and Stage 4P to look for any

anomalies or inconsistencies with the instrument norms. I found the pre- and post test

scores to be nearly identical with no statistically significant changes or unusual scores.

Descriptive statistics including mean, median, mode, skewness, and standard deviation

were calculated for both the pre- and posttest data. A t-Test (95% C.L.), and Pearson


correlation were calculated and reported here for matched pairs of the Postconventional,

N2, and Utilizer scores.


Chapter 4

Results

The primary purpose of this research is to explore the role reflecting on and

sharing values plays in our individual decision-making schemas in groups. Specifically to

answer the question: Can introducing values into decision considerations disrupt existing

unconscious schemas? To accomplish this goal, the Defining Issues Test 2 (DIT2) was

used. It measures the unconscious values schemas individuals use in their decision-

making that determine their level of ethical decision-making. It is important to note that

participants were not told the instrument measured ethical decision-making. They were

simply told the instrument measured how they make decisions. The brief activity in

which they were asked to determine the values associated with the decision

considerations in Scenario 1 was the first time a values frame was introduced in the

decision-making process.

There were a total of 135 participants in the experiments with 107 (79.26%)

complete matched pretest/posttest sets after scoring. Participants were removed from the

study if they did not have completed pre- or posttests, including incomplete demographic

information required by the scorer, or if they had “meaningless” scores higher than 10. A

significant portion of the participants removed from the study came from Group 4. The

group originally had 45 participants, but had 21 (47%) of participants removed due to one

of the reasons discussed above. Group 4 was unique in that the class period was only 50

minutes in length, whereas other groups used 1 1/4 to 1 1/2 hours to complete the

experiment. For many participants, 50 minutes was not enough time to complete the

experiment adequately as they had other classes to attend across a very large land grant


university. However, some students did stay after the 50-minute class period to complete

their posttest. Incomplete or meaningless posttests were the primary reason for rejecting

participant responses in this case. This was not a reflection of the method, but the time

limitation available for participants.

All participants described themselves as ethical decision-makers, and only 3

participants reported they did not use their personal values when making work decisions.

No one was allowed to participate more than once even if they were in more than one of

the classes utilized. The experiment was conducted 5 times and information about the

groups is described below.

For those reporting demographic information for all participants the age range

was from 18 to 50 with a mean age of 24.5, median of 22, and a mode of 21. Sixty-five

(62%) participants were female and 40 (38%) were male. Seventy-four (71%) students

are “traditional” college age students (18 – 24) and 31 (30%) are “nontraditional” college

age students (25+). Of the scored participants reporting, 34 report a management major,

16 accounting majors, 13 dual majors in management and marketing, 8 hospitality

management majors, 4 computer science majors, 3 nursing majors, 3 logistics and supply

chain management majors, 3 marketing majors, 3 organization and innovation majors, 2

mechanical engineering majors, 2 dual majors in management and accounting, 2

computer information systems majors, and 1 major each in human resources, computer

information and office systems, human services, dental hygiene, business and music,

accounting and global logistics management, management and finance, management and

entrepreneurship, management and human resources, management and interior design,

and management and equine science.


Group Descriptions - UAA Mat-Su College

Group 1

This group was a MSC lower division Microeconomics class and consisted of 13

participants resulting in 11 (85%) complete scored matched pretest/posttest pairs.

Computer problems during the completion of either a pre- or posttest resulted in

incomplete results in both cases. All scored participants are female, 2 are non-U.S.

citizens and 3 are non-native English speakers. Ages of the group range from 19 to 45

with a mean age of 27.36, median of 27, and a mode of 19. Five students (45%) are

“traditional” college age students (18-24) and 6 (55%) are “nontraditional” college age

students (25+). Of the scored participants reporting, 7 are accounting majors, 3 business

administration majors, and one mechanical engineering major.

The distribution of students reporting length of time as part of the cohort is on Table 4.1

on page 74

Due to the small class size, rather than taking a random sample of the values

students assigned to each of the decision considerations in Scenario 1, all student

responses were shared. The shared value set for each of the Scenario 1 decision

considerations for all groups is in Appendix E. Duplicate responses were removed from

the set.

Group 2

This group was a MSC lower division second semester Principles of Financial

Accounting class and consisted of 20 participants resulting in 20 (100%) complete scored

matched pretest/posttest pairs. Five scored participants are male and 15 are female, all are


U.S. citizens and native English speakers. Ages of the group range from 18 to 48 with a

mean age of 29.3, median of 27, and a mode of 26. Five students (25%) are “traditional”

college age students (18-24) and 15 (75%) are “nontraditional” college age students

(25+). Of the scored participants reporting, 9 are accounting majors, 8 are business

administration/management majors, 1 human resources major, 1 computer information

and office systems/office administration major, 1 nursing major, and 1 double major in

accounting and global logistics management. The distribution of students reporting length

of time as part of the cohort is on Table 4.1 on page 74.

For this class a random sample of 10 student responses to the values students

assigned to each of the decision considerations in Scenario 1 were shared. Duplicate

responses were removed from the set.

Group 3

This group was a MSC lower division Supervision class and consisted of 19

participants resulting in 17 (89%) complete scored matched pretest/posttest pairs. Eleven

scored participants are male and 6 are female, all are U.S. citizens and 1 is a non-native

English speaker. For those reporting (16), ages of the group range from 19 to 50 with a

mean age of 25.93, median of 23.5, and a mode of 22. Eight students (50%) are

“traditional” college age students (18-24) and 8 (50%) are “nontraditional” college age

students (25+). Of the scored participants reporting, 4 are computer science majors, 3

logistics and supply chain management majors, 2 business management majors, 2 nursing

majors, 2 undecided, 1 human service major, 1 dental hygiene major, and 1 dual business

and music major. The distribution of students reporting length of time as part of the

cohort is on Table 4.1 on page 74.



assigned to each of the decision considerations in Scenario 1 were shared. Due to the time

constraints experienced with Groups 4 and 5 at CSU, a sample of 5 student responses was

chosen for those groups. I decided to choose only 5 from this group as well for

consistency with the CSU groups. Doing so still provided a variety of responses (see

Appendix E). Duplicate responses were removed from the set.

Group Descriptions - Colorado State University

Group 4

This group was a CSU upper division Human Resources class and consisted of 45

participants resulting in 24 (53%) complete scored matched pretest/posttest pairs. Of

those scored participants reporting (22), 10 are male and 12 are female, 1 is not a U.S.

citizens and 1 is a non-native English speaker. For those reporting (16), ages of the group

range from 20 to 36 with a mean age of 22.09, median of 21, and a mode of 21. Twenty

students (91%) are “traditional” college age students (18-24) and 2 (9%) are

“nontraditional” college age students (25+). Of the scored participants reporting, 16

report a business administration major (7 management, 2 computer information systems,

3 marketing, 3 organization and innovation management) and 8 are hospitality

management majors. The distribution of students reporting length of time as part of the




constraints experienced with Groups 4 and 5 at CSU, a sample of 5 student responses to


the values students assigned to each of the decision considerations in Scenario 1 were

shared (see Appendix E). Duplicate responses were removed from the set.

Group 5

This group was a CSU upper division Organizational Behavior and Leadership class and

consisted of 36 participants resulting in 35 (97%) complete scored matched

pretest/posttest pairs. Of those scored participants reporting (22), 14 are male and 21 are

female, 1 is not a U.S. citizens and 1 is a non-native English speaker. For those reporting

(16), ages of the group range from 20 to 24 with a mean age of 21.74, median of 22, and

a mode of 22. Thirty-five students (100%) are “traditional” college age students (18-24)

and none are “nontraditional” college age students (25+). Of the scored participants

reporting, 14 report a management major, 13 are management and marketing majors, 3

management and finance majors, 1 management and entrepreneurship major, 1

management and HR major, 2 management and accounting majors, 1 management and

interior design major, 1 management and equine science major, and 1 mechanical

engineering major. The distribution of students reporting length of time as part of the




constraints experienced with Groups 4 and 5 at CSU, a sample of 5 student responses to

the values students assigned to each of the decision considerations in Scenario 1 were

shared (see Appendix E). Duplicate responses were removed from the set.


Demographic Summary

Table 4.1 on the next page provides a summary of demographic information by

group.


Table 4.1

Demographic Summary

Age Traditional/Non-

Traditional Student

Gender

Group Scored

Participants

Range Mean Median Mode Trad. Nontrad. Female Male

1 11 19-45 27.36 27 19 5(45%) 6(55%) 11 0

2

20 18-48 29.3 27 26 5(25%) 15(75%) 15 5

3

17 19-50 25.93 23.5 22 8(50%) 8(50%) 6 11

4

24 20-36 22.09 21 21 20(91%) 2(9%) 12 10

5

35 20-24 21.74 22 22 35(100%) 0(0%) 21 14


Length of Time in Cohort

Table 4.2 below shows the time students reported being part of their cohort of

students. An explanation of how cohorts are defined in this context follows the table.

Table 4.2

Length of Time in Cohort

Group Years Frequency

1 <1 1

1 3

2 4

3 1

4 1

>4 1

2 <1 7

1 5

2 4

3 4

4 0

>4 0

3 <1 4

1 6

2 4

3 2

4 1

>4 0

4 <1 0

1 2

2 5

3 10

4 0

>4 0

5 <1 3

1 0

2 5

3 5

4 9

>4 2

While these students were not in traditional cohorts (i.e. moving through all their

classes together), they were part of lower or upper division groups. At MSC students are


Freshman and Sophomores taking the same business classes and there is often only 1

section to choose from. CSU students were Juniors and Seniors and are taking many of

their upper division classes together. Most students also live on campus and so have

social relationships outside of class time with their classmates.

DIT2 Measured Scores Data

Again, the primary purpose of this research is to explore the role reflecting on and

sharing values plays in our individual decision-making schemas in groups. Specifically,

whether introducing values into decision considerations can disrupt existing unconscious

schemas. To accomplish this goal, the DIT2 was utilized to measure schema scores as

they relate to the level of ethical decision-making development. For purposes of this

research the following DIT-2 scores were analyzed:

1) Postconventional Schema Score (P Score). This score measures how much of

the decision-making process falls into the Stages 5 and 6 categories of Kohlberg’s

value development scale. As the description states, this is the Postconventional

stage of value development. An increase in score indicates an improvement

Postconventional ethical decision-making.

2) New Index Score (N2 Score). The N2 score is designed to measure: a) “The

degree to which Postconventional items are prioritized”, and b) “The degree to

which Personal interest items (lower stage items) receive lower ratings than the

ratings given to Postconventional items (higher stage items)” (Bebeau & Thoma,

2003, p. 19). It provides an overall level of value development, not just a schema

score. An increase in score indicates an improvement in Postconventional ethical


decision-making and movement away from lower stage (Personal interest) ethical

decision-making.

3) Utilizer Score (U Score). This score “represents the degree of match between

items endorsed as most important and the action choice on that story” (Bebeau

and Thoma, 2003, p. 21). In other words, how well the participant selected a

menu of decision considerations that were directly related to their decision.

As the number of participants in all but one of the groups was less than 30, I have

decided to discuss the results by school and for all participants. I have looked at the

statistics at the group level and did not find any significant data that was inconsistent with

the school and all participant levels. For reference the N2 Score Descriptive statistics and

t-Test results by group are attached in Appendix F. While not relevant to the results being

reported in this study, the lower stage scores (Stage 23 and 4P Scores) were analyzed by

group, school, and all data to look for any abnormalities that might indicate students were

performing at a level lower than what would be consistent with their age group. No

abnormalities or significant pretest/posttest changes were observed. For reference,

descriptive and t-Test data for the Stage 23 and 4P Score is available in Appendix G.

MSC Scores

Postconventional schema score. Descriptive data for MSC is listed below.

Table 4.3

MSC Postconventional Schema Score Descriptive Statistics

Pscore Pre MSC Pscore Post MSC

Mean 30.25 Mean 32.33333333

Standard Error 1.854288684 Standard Error 1.775855935

Median 30 Median 34


Mode 38 Mode 36

Standard

Deviation 12.84688885

Standard


Sample Variance 165.0425532 Sample Variance 151.3758865

Kurtosis 0.265591982 Kurtosis 0.109293307

Skewness 0.370817737 Skewness 0.180855038

Range 62 Range 58

Minimum 4 Minimum 6

Maximum 66 Maximum 64

Sum 1452 Sum 1552

Count 48 Count 48

A test for skewness indicates the data is approximately normally distributed (see

Figures 4.1 and 4.2 below).

Figure 4.1. MSC PScore Prettest Figure 4.2. MSC PScore Posttest

The data is highly positively correlated with a Pearson Correlation of 0.75. A two-

tailed correlated t-test found a critical t of 2.01 and an observed t Stat of -1.64 suggesting

the 2.08-point increase in the mean values is not statistically significant. A two-tailed p-

value of 0.11 supports a failure to reject the null hypothesis for the MSC

Postconventional Schema Score.

N2 Index score. Descriptive data for MSC is listed on the next page.

0 20 40 0 20 40


Table 4.4

MSC N2 Index Score Descriptive Statistics

N2 Score Pre MSC N2 Score Post MSC

Mean 27.94360339 Mean 32.26344346


Median 28.33984424 Median 34.93098236

Mode #N/A Mode #N/A

Standard Deviation 12.61099407 Standard Deviation 14.41375295


Kurtosis -0.973468014 Kurtosis -0.898486707

Skewness -0.189759437 Skewness 0.120923696

Range 50.39917637 Range 57.82962879

Minimum 1.662813234 Minimum 6.446759816

Maximum 52.06198961 Maximum 64.2763886

Sum 1341.292962 Sum 1548.645286

Count 48 Count 48



Figure 4.3. MSC N2Score Pretest Figure 4.4. MSC N2Score Posttest



the 4.32-point increase in the mean values is statistically significant. A two-tailed p-value

of 0.002 supports a rejection of the null hypothesis for the MSC New Index Score.

0 10 20 30 40 50 -10 10 30 50


Consistent with Cohen’s (1992) use of Pearson’s Correlation as a test of effect size, these

results indicate a large effect size.

Utilizer score. For the utilizer score n was reduced from 48 to 47. This is

because the data used to calculate the score is different from the other scores, and one

participant had incomplete data in this category. Descriptive data for MSC is listed

below.

Table 4.5

MSC Utilizer Score Descriptive Statistics

Utilizer Pre MSC Utilizer Post MSC

Mean 0.204736493 Mean 0.159430344


Median 0.207656004 Median 0.162821185

Mode #N/A Mode 0

Standard


Standard





Range 0.542737284 Range 0.688368165

Minimum 0.080230729 Minimum 0.174471993

Maximum 0.462506555 Maximum 0.513896172

Sum 9.622615184 Sum 7.493226151

Count 47 Count 47


Figures 4.5 and 4.6 on the next page).


Figure 4.5. MSC Utilizer Pretest Figure 4.6 MSC Utilizer Posttest


tailed correlated t-test found a critical t of 2.01 and an observed t Stat of 3.05 suggesting

the 0.04-point decrease in the mean values is statistically significant. A two-tailed p-value

of 0.004 supports a rejection of the null hypothesis for the MSC Utilizer Score.

Consistent with Cohen’s (1992) use of Pearson’s Correlation as a test of effect size, these

results indicate a large effect size.

CSU Scores

Postconventional schema score. Descriptive data for CSU is listed below

Table 4.6

CSU Postconventional Schema Score Descriptive Statistics

0 20 400 20 40

Pscore Pre CSU Pscore Post CSU

Mean 35.05084746 Mean 31.79661017


Median 34 Median 30

Mode 42 Mode 24

Standard


Standard





Range 62 Range 76

Minimum 10 Minimum 0





Figure 4.7. CSU PScore Pretest Figure 4.8 CSU PScore Posttest



the 3.25-point decrease in the mean values is not statistically significant and supports a

failure to reject the null hypothesis for the CSU Postconventional Schema Score.

However, two-tailed p-value of 0.06 indicates a rejection of the null hypothesis for α =

.10. Consistent with Cohen’s (1992) use of Pearson’s Correlation as a test of effect size,

these results indicate a large effect size.

N2 Index score. Descriptive data for CSU is listed on the following page.

0 20 400 20 40

Sum 2068 Sum 1876

Count 59 Count 59


Table 4.7

CSU N2 Index Score Descriptive Statistics

N2 Score Pre CSU N2 Score Post CSU

Mean 32.99772139 Mean 31.47021292


Median 33.72876867 Median 33.5596598

Mode #N/A Mode #N/A



Kurtosis -0.461154555 Kurtosis -0.570050312


Range 61.14372514 Range 71.79608888

Minimum 5.300049497 Minimum 1.186909712

Maximum 66.44377464 Maximum 72.98299859

Sum 1946.865562 Sum 1856.742562

Count 59 Count 59



Figure 4.9. CSU N2Score Pretest Figure 4.10. CSU N2Score Posttest




value of 0.31 supports a failure to reject the null hypothesis for the CSU New Index

Score.

0 20 40 600 20 40 60


Utilizer score. For the utilizer score n was reduced from 59 to 56. This is

because the data used to calculate the score is different from the other scores, and 3

participants had incomplete data needed to calculate the score in this category.

Descriptive data for CSU is listed below.

Table 4.8

CSU Utilizer Score Descriptive Statistics

Utilizer Pre CSU Utilizer Post

Mean 0.126362085 Mean 0.094170655


Median 0.095107623 Median 0.089472952

Mode 0 Mode 0

Standard


Standard





Range 0.636753612 Range 0.783210405

Minimum -0.11341853 Minimum 0.382275826

Maximum 0.523335081 Maximum 0.400934579

Sum 7.076276769 Sum 5.273556698

Count 56 Count 56

A test for skewness indicates the data is moderately skewed (see Figures 4.11 and

4.12 on the next page).


Figure 4.11. CSU Utilizer Pretest Figure 4.12. CSU Utilizer Posttest

The data has low to moderate positive correlation with a Pearson Correlation of

0.33. A two-tailed correlated t-test found a critical t of 2.00 and an observed t Stat of 1.54

suggesting the 0.03-point decrease in the mean values is not statistically significant. A

two-tailed p-value of 0.13 supports a failure to reject the null hypothesis for the CSU

Utilizer Score.

All Data Scores

All scores were analyzed at the “all data” level for all groups and all schools. In

the areas were there were not inconsistencies across schools (Personal Interest and

Maintaining Norms Scores), none were found at the all data level. As some of the school

level statistics for the Postconventional Schema, New Index, and Utilizer Scores suggests

a rejection of the null hypothesis, the results at the all data level for those scores are

presented here for comparison.

Postconventional schema score. Descriptive data for all data (n=107) is listed

on the following page.

0 20 40 0 20 40


Table 4.9

All Data Postconventional Schema Score Descriptive Statistics

Pscore Pre All Data Pscore Post

Mean 32.89719626 Mean 32.03738318


Median 30 Median 32

Mode 42 Mode 36

Standard


Standard





Range 68 Range 76

Minimum 4 Minimum 0


Sum 3520 Sum 3428

Count 107 Count 107



Figure 4.13. All Data PScore Pretest Figure 4.14. All Data PScore Posttest




value of 0.44 supports a failure to reject the null hypothesis for all data in the

Maintaining Norms Schema Score

0 50 100 0 50 100


N2 Index score. Descriptive data for CSU is listed below.

Table 4.10

All Data N2 Index Score Descriptive Statistics

N2 Score Pre All Data N2 Score Post All Data

Mean 30.7304535 Mean 31.82605466


Median 31.29248276 Median 34.19921192

Mode #N/A Mode #N/A

Standard

Deviation 13.82500455 Standard Deviation 16.05091784




Range 64.78096141 Range 71.79608888

Minimum 1.662813234 Minimum 1.186909712

Maximum 66.44377464 Maximum 72.98299859

Sum 3288.158524 Sum 3405.387849

Count 107 Count 107



Figure 4.15. All Data N2Score Pretest Figure 4.16. All Data N2Score Posttest



0 50 100 0 50 100



value of 0.30 supports a failure to reject the null hypothesis for all data in the New Index

Score.

Utilizer score. Descriptive data for all data (n = 103) is listed below.

Table 4.11

All Data Utilizer Score Descriptive Statistics

Utilizer Pre All Data Utilizer Post

Mean 0.162125165 Mean 0.123949348


Median 0.134242265 Median 0.114840063

Mode 0 Mode 0

Standard


Standard





Range 0.636753612 Range 0.896171998

Minimum -0.11341853 Minimum 0.382275826

Maximum 0.523335081 Maximum 0.513896172

Sum 16.69889195 Sum 12.76678285

Count 103 Count 103

A test for skewness indicates the data is approximately normally distributed (see Figures

4.17 and 4.18 on the next page).


Figure 4.17. All Data Utilizer Pretest Figure 4.18. All Data Utilizer Posttest

The data is moderately positively correlated with a Pearson Correlation of 0.55. A

two-tailed correlated t-test found a critical t of 1.98 and an observed t Stat of 2.89

suggesting the 0.04-point decrease in the mean values is statistically significant. A two-

tailed p-value of 0.005 supports a rejection of the null hypothesis for all data in the

Utilizer Score. Consistent with Cohen’s (1992) use of Pearson’s Correlation as a test of

effect size, these results indicate a large effect size.

Number of Values Shared Scores

As discussed previously, time limitations did not allow as many values to be

shared in Groups 4 & 5 (CSU). To have a MSC equivalent, Group 3 also shared 5 value

sets, although time was not an issue with this group. The following tests for differences in

results based on sharing 10 or more and only 5 of the student responses to the values

expressed exercise for the Postconventional Schema, New Index, and Utilizer Scores.

Postconventional schema score. Descriptive data for 10+ (n = 31) and 5 (n = 76)

responses shared are listed on the following page.

0 50 100 0 50 100


Table 4.12

Shared 10+ Postconventional Schema Score Descriptive Statistics

Pscore Pre Shared 10+ Pscore Post Shared 10+

Mean 31.29032258 Mean 32.70967742


Median 32 Median 32

Mode 32 Mode 36

Standard


Standard





Range 54 Range 54

Minimum 4 Minimum 10


Sum 970 Sum 1014

Count 31 Count 31

Table 4.13

Shared 5 Postconventional Schema Score Descriptive Statistics

Pscore Pre Shared 5 Pscore Post Shared 5

Mean 33.55263158 Mean 31.76315789


Median 30 Median 32

Mode 42 Mode 34

Standard


Standard



Kurtosis 0.436529452 Kurtosis -0.39143388


Range 64 Range 76

Minimum 8 Minimum 0


Sum 2550 Sum 2414

Count 76 Count 76



Figures 4.19 through 4.22 below).

Figure 4.19. 10+ PScore Pretest Figure 4.20. 10+ PScore Posttest

Figure 4.21. 5 PScore Pretest Figure 4.22. 5 PScore Posttest

The data is highly positively correlated with a Pearson Correlation of 0.84 for the

10+ group and 0.70 for the 5 group. A two-tailed correlated t-Test found a critical t of

2.04 for the 10+ group and an observed t Stat of -1.17 suggesting the 1.42-point increase

in the mean values is not statistically significant. A two-tailed p-value of 0.25 supports a

failure to reject the null hypothesis for the 10+ Maintaining Norms Schema Score.

A two-tailed correlated t-Test found a critical t of 1.99 for the 5 group and an

observed t Stat of 1.22 suggesting the 1.79-point decrease in the mean values is not

statistically significant. A two-tailed p-value of 0.23 supports a failure to reject the null

hypothesis for the 5 Maintaining Norms Schema Score.

0 10 20 30 0 10 20 30

0 20 40 60 0 20 40 60


N2 Index score. Descriptive data for 10+ (n = 31) and 5 (n = 76) responses

shared are listed below.

Table 4.14

Shared 10+ N2 Index Score Descriptive Statistics

N2Score Pre Shared 10+ N2Score Post Shared 10+

Mean 28.38912904 Mean 31.72975939


Median 29.91984919 Median 35.73612658

Mode #N/A Mode #N/A

Standard


Standard



Kurtosis 0.840937711 Kurtosis -1.33088735


Range 50.39917637 Range 51.9509142

Minimum 1.662813234 Minimum 6.446759816

Maximum 52.06198961 Maximum 58.39767402

Sum 880.0630002 Sum 983.6225411

Count 31 Count 31

Table 4.15

Shared 5 N2 Index Score Descriptive Statistics

N2Score Pre Shared 5 N2Score Post Shared 5

Mean 31.68546742 Mean 31.86533299


Median 31.40542903 Median 33.62197303

Mode #N/A Mode #N/A

Standard


Standard



Kurtosis -0.44118115 Kurtosis 0.414103569


Range 61.14372514 Range 71.79608888

Minimum 5.300049497 Minimum 1.186909712

Maximum 66.44377464 Maximum 72.98299859


Sum 2408.095524 Sum 2421.765308

Count 76 Count 76



Figure 4.23. 10+ NScore Pretest Figure 4.24. 10+ NScore Posttest

Figure 4.25. 5 NScore Pretest Figure 4.26. 5 NScore Posttest

The data is highly positively correlated with a Pearson Correlation of 0.88 for the

10+ group and 0.70 for the 5 group. A two-tailed correlated t-Test found a critical t of

2.04 for the 10+ group and an observed t Stat of -2.58 suggesting the 3.34-point increase

in the mean values is statistically significant. A two-tailed p-value of 0.02 supports a

rejection of the null hypothesis for the 10+ N2 Index Score. Consistent with Cohen’s

(1992) use of Pearson’s Correlation as a test of effect size, these results indicate a large

effect size.

A two-tailed correlated t-Test found a critical t of 1.99 for the 5 group and an

observed t Stat of -0.13 suggesting the 0.18-point increase in the mean values is not

0 10 20 300 10 20 30

0 20 40 60 0 20 40 60


statistically significant. A two-tailed p-value of 0.90 supports a failure to reject the null

hypothesis for the 5 N2 Index Score.

Utilizer score. Descriptive data for 10+ (n = 30) and 5 (n = 76) responses shared

are listed on the following page.

Table 4.16

Shared 10+ Utilizer Score Descriptive Statistics

Utilizer Pre Shared 10+ Utilizer Post Shared 10+

Mean 0.221827634 Mean 0.172362043


Median 0.221399887 Median 0.163756566

Mode #N/A Mode 0

Standard


Standard




Skewness 0.157485846 Skewness -0.27983185

Range 0.450668549 Range 0.595552223

Minimum 0.011838006 Minimum 0.174471993

Maximum 0.462506555 Maximum 0.421080231

Sum 6.654829024 Sum 5.170861277

Count 30 Count 30

Table 4.17

Shared 10+ Utilizer Score Descriptive Statistics

Utilizer Pre Shared 5 Utilizer Post Shared 5

Mean 0.137589903 Mean 0.10405372


Median 0.106492478 Median 0.100943891

Mode 0 Mode 0

Standard


Standard






Range 0.636753612 Range 0.896171998

Minimum -0.11341853 Minimum 0.382275826

Maximum 0.523335081 Maximum 0.513896172

Sum 10.04406293 Sum 7.595921572

Count 73 Count 73



Figure 4.27. 10+ Utilizer Pretest Figure 4.28. 10+ Utilizer Posttest

Figure 4.29. 5 Utilizer Pretest Figure 4.30. 5 Utilizer Posttest

The 10+ data is highly positively correlated with a Pearson Correlation of 0.67,

but the 5 group is only moderately correlated at 0.48. A two-tailed correlated t-Test found

a critical t of 2.05 for the 10+ group and an observed t Stat of 2.70 suggesting the 0.05-

point decrease in the mean values is statistically significant. A two-tailed p-value of 0.01

supports a rejection of the null hypothesis for the 10+ Utilizer Score.

A two-tailed correlated t-Test found a critical t of 1.99 for the 5 group and an observed t

Stat of 1.96 suggesting the 0.04-point decrease in the mean values is just barely not

0 10 20 30 0 10 20 30

0 20 40 60 0 20 40 60


statistically significant. A two-tailed p-value of 0.05 supports a rejection of the null

hypothesis for the 5 N2 Index Score.


Results Summary

The chart below is a summary by data level and index of the results described in this chapter. The summary includes

the recommendation to reject or fail to reject the null hypothesis.

Table 4.18

Results Summary

Data Level Index

Pearson

Correlation

Critical

t Observed t p-value

Reject the Null

Hypothesis?

Effect

Size

MSC Postconventional 0.75 2.01 -1.64 0.110 Fail to reject N/A

N2 0.77 2.01 -3.22 0.002 Yes Large

Utilizer 0.74 2.01 3.05 0.004 Yes Large

CSU Postconventional 0.73 2.00 1.95 0.060 Yes at 90% C.L. Large

N2 0.75 2.00 1.02 0.310 Fail to reject N/A

Utilizer 0.33 2.00 1.54 0.130 Fail to reject N/A

All Data Postconventional 0.72 1.98 0.78 0.440 Fail to reject N/A

N2 0.74 1.98 -1.03 0.300 Fail to reject N/A


10+

Shared Postconventional 0.84 2.04 -1.17 0.250 Fail to reject N/A

N2 0.88 2.04 -2.58 0.020 Yes Large


5 Shared Postconventional 0.70 1.99 1.22 0.230 Fail to reject N/A

N2 0.70 1.99 -0.13 0.900 Fail to reject N/A



Chapter 5

Discussion

According to Rest et al. (2000), over 25 years of data—with approximately 13,

386 responses with 10, 870 included in the analysis—have found test-retest results

without interventions to be unchanged for all groups. Researchers have also found that

DIT scores “show significant gains due to moral educational programs of more than 3

weeks” and during the college years in liberal arts programs (Bebeau & Thoma, 2003).

This study is unique in that the intervention is very short, does not attempt to “teach”

participants about values or ethical decision-making, and is largely personal in its impact

as there is no discussion, only sharing. This supports the exploratory nature of the

research. The intervention does reframe decision-making in value terms and brings the

assignment of values to decision considerations to the conscious level for individuals and

the group. Yet this minor intervention did cause some statistically significant changes in

scores.

Postconventional and N2 Scores

When the results are analyzed for all participants together, there is little change in

the Postconventional and N2 Scores, however there are some interesting and significant

results when we look at the schools individually. MSC students improved their N2 Scores

by a statistically significant 4.32 points. This indicates they shifted their use of lower

stage items to Postconventional items and became better ethical decision-makers. They

also increased their Postconventional scores by 2.08 points. Tests for statistical

significance were not quite enough to support statistical significance at a 90% confidence


level (p-value 0.11) for the Postconventional scores, but it does contribute to the upward

trend of MSC scores reflected in the N2 score.

CSU students, by contrast, saw a decrease of 3.25 points in their Postconventional

scores indicating the amount of their decision-making in Stages 5 and 6 may have

decreased as these results are only statistically significant at a 90% confidence level. The

student’s N2 scores decreased by 1.53 points, which was not a statistically significant

change. At a 95% confidence level the results indicate that the CSU groups scores were

relatively unchanged.

Norm Comparisons Postconventional and N2 Scores

Assuming the CSU scores were unchanged or only had a very small change, what

might have contributed to no change or a small change in ethical decision-making at CSU

while there were significant improvements at MSC? The DIT norms suggest several

factors that correlate with scores that might give us some clues.

Cognitive development. DIT scores are often positively correlated with cognitive

development including IQ, general intelligence, achievement, and GPA. While GPA data

on all participants was not available, we can make some inferences about the cognitive

development of our groups. CSU is a selective school and according to the Colorado

State University Profile (2013), students admitted to the university are in the 74th

percentile of their graduating class, have an average high school GPA of 3.59, an ACT

composite score of 24.7, and/or a SAT combined score of 1142. MSC is an open

enrollment school and does not have any gates for class rank, high school GPA, or ACT

or SAT scores. We can infer from their open enrollment status that MSC will have a

much broader range of student ability than CSU. MSC students are required to take a


math and English placement test and with only 75% of students entering the school with

college level math and/or reading skills, we can also assume the level of cognitive

development of MSC students entering the school is lower than CSU.

Because of this difference, norms indicate that CSU students would have higher

scores on the DIT than MSC students. On the pretest, this was true with a CSU

Postconventional score of 35.05 compared to MSC’s at 30.25 and N2 Scores of 33.00

(CSU) and 27.94 (MSC). However, cognitive development fails to explain the posttest

results as MSC’s posttest scores for both the Postconventional and N2 Scores were above

CSU’s and overall closer in value with CSU than the pretest scores were.

Education. A second factor that is often correlated with DIT scores is level of

education. Related to college students the norms indicate junior and senior level students

will outperform freshman and sophomore level students. Again, this factor fails to

explain the posttest scores as the CSU students were juniors and seniors in upper division

classes and should have outperformed the MSC students who were largely freshman and

sophomores in lower division classes.

Gender. Gender is another factor that often predicts performance on

Postconventional scores. Norms indicate female participants generally score higher than

males. In this study 67% of MSC participants were female and 58% at CSU. Based on

these ratios we might expect the MSC group to consistently outscore the CSU group, but

that was not the case. However, the N2 Score results for MSC may have been impacted

by the high percentage of women in the group. When looked at alone, female participants

increased their mean score by a statistically significant 5.18 points (t-critical 2.04,

observed t -3.26, p-value .003) compared to the male participants who increased their


score by a statistically insignificant 2.61 points. Similar gender differences were not

observed at CSU.

It is possible that traditionally higher scores by women could indicate more

sensitivity to values and ethics in decision-making then men and/or that women are more

likely to adjust individual decision-making in a group when values are shared. Several

studies support the possibility of either of these factors influencing ethics scores. A study

on business students by Stedham et al. (2007) found that women have a stronger “intent”

to behave in an ethical manner so by reframing the decision-making process in values

terms, the study may have engaged the ethical decision-making perspective to a higher

degree for women than it did for the male participants as females appear to be more

sensitive to subtle ethical context than males are. Studies have also indicated that women

“focus their [ethical] analysis on personal, relationship-oriented aspects of an action”

(Stedham et al., 2007, p. 171) and are less likely to prefer competitive success and more

likely to promote harmonious work relationships and engage in social learning (Ameen,

Guffey, & McMillan, 1996). While men are more likely to focus on clear-cut objective

criteria in ethical decision-making, women are more likely to consider ‘relative’

considerations (Stedham et al., 2007). Female students are also more likely to engage in

impression management than males so by reframing decision-making in a values/ethics

framework, female students may be more likely to answer in a way that will give the

impression of being ethical (Becker & Ulstad, 2007). The fact that there were not similar

results for N2 or P Scores at CSU might indicate that gender was one, but not the only

factor that caused women at MSC to improve their scores more dramatically than men.

Non-Normed Factors for Postconventional and N2 Scores


Reviewing the norms associated with the DIT-2 did not sufficiently explain our

results. As this research is utilizing an intervention that appears to be significantly

different than the pre- and posttest experiments in the past, it is possible that the norms do

not apply as strongly to this research. The following are some other factors to consider

that might help explain the results.

Group cognitive “sameness” and number of values shared. One issue that

needs to be considered is to what degree the participants in the groups were similar

before the experiment and how that may have impacted results. As discussed in the

“Cognitive Development” section, there are reasons to believe the individual’s in the

CSU group were more alike than the individual’s in the MSC group. The CSU students

all went through a competitive admission process that was looking for similar

characteristics, intellectual capacity, etc. The MSC students were part of an open

enrollment campus and provide a much more diverse, although more geographically

isolated group.

As we assume decision-making is a cognitive process, it is possible that the

cognitive “sameness” of the individuals in the CSU classes impacted their ability to be

influenced by the sharing of values, because there values were less diverse to begin with

as they shared a similar cognitive decision-making process. To test for “sameness” I

looked at the three groups that only shared 5 sets of values and compared the average

number of distinct values shared per decision consideration. The groups included both

CSU groups (Groups 4 & 5) and one MSC group (Group 3). I found that the MSC group

averaged 4.4 values per decision consideration and CSU Group 4 averaged 4.2 and Group


5 averaged 3.5. This does provides some evidence that the CSU groups, especially Group

5 had more similar values systems prior to the experiment than MSC students did.

Another factor is that students in the CSU classes only shared 5 random sets of

values, whereas 2 of 3 MSC classes shared 13 and 10. When the data was analyzed using

the number of sets of values shared, the “10+” group (n = 31) had a statistically

significant increase in the N2 Score, whereas the “5” group (n = 76) had no significant

changes. This data supports a possibility that the number and diversity of the values

shared might also make a difference in future decision-making; however, additional

research needs to be done to verify these potential correlations.

Relationships. Another factor the results lead us to consider is the potential role

of relationships. Much of the literature reviewed on the values ether (Bell, 2011b),

behavioral economics/finance (Ariely, 2008; Sen 1977; 1994; Thaler 1988; Thaler &

Sunstein, 2009) value development (Schwartz, 1992; Kohlberg, 1973; Babeau, et al,

1999), social learning theory (Bandura, 1977), and sensemaking in organizations (Weick,

1995; Weick et al., 2005) suggest relationships have an impact on values in decision-

making. It is possible to infer from the distinct differences in the groups that there may be

some different levels of relationships between the CSU and MSC students. According to

the Colorado State University Profile (2013), students at CSU come from every state in

the country with 80% being Colorado residents. The campus and community are large

both in geographic and population terms and students are primarily of traditional college

age (24 and under). By contrast, MSC students are primarily local with 96% of the

student population coming from Alaska and 44% graduating from high schools in one the

MSC local area communities. The campus and the student population are small as is the


number of full-time faculty (29). Only 57% of MSC students are of traditional age and

43% are non-traditional (University of Alaska Planning and Institutional Research, 2011).

The differences between the two campuses make it likely that MSC students may

have stronger and longer relationships with each other and faculty and staff than CSU

students do. These relationships may extend beyond the campus and into the local

community. Many of the MSC students have known each other even before attending the

university and the small campus size allows them to get to know a greater percentage of

the other students, faculty and staff. Smaller class sizes also allow students to form

relationships and get to know the perspectives of other students through class discussions.

The size of the individual groups might also be a factor in this research as it relates to

relationship building. The MSC students are not only more likely to come from the same

small community, but are also generally older so they may have known each other for a

longer period of time. For students reporting their length of time with their cohort, the

majority of MSC students (78.7%) report being part of their cohort for 2 years or less,

whereas the majority of CSU students (63%) report a 3 or more year relationship with

their cohort. If relationships were a factor, it does not appear that the shorter length of

time in the MSC cohort impacted their relationships in a negative way indicating that

perhaps other factors strengthen relationships of university students.

The issue of relationships also ties back to the discussion of gender. As the

majority of participants at MSC were female—and were the group primarily responsible

for the N2 Score increase—we need to consider the combination of gender and

relationships as a possible explanation for the differences in scores between MSC and

CSU students. In a previous section I discussed how women might be more sensitive to


values and ethics in decision-making and have stronger intent to behave ethically. They

are also more likely to analyze their decision-making relative to others, engage in social

learning, and be concerned with harmonious relationships. By reframing the discussion of

decision-making in a value context and by and reflecting on and sharing values, it is

possible that females in small groups within which they have strong relationships might

have been more likely to move toward more shared ethical decision-making than women

who are in groups that do not have strong relationships or are in some way moderated by

an equal male presence.

Utilizer Scores

Another data point that is significant to this study is the utilizer score (U Score).

The U Score measures the degree of match between the action participants said they

would take in each of the scenarios and the decision considerations they rated as most

important. The test assumes certain decision considerations, when important to the

decision-maker, will lead to a specific action. For every data level with the exception of

the CSU group alone, the U Scores dropped by a statistically significant amount posttest.

One possible reason for this decrease might be a limitation of the test itself. This

score is essentially designed to test the ability of participants to rationally analyze

available factors and match them to decision-making. The test assumes rationality and

access to all information, as the scope of available information is provided in the scenario

and given decision considerations. It does not assume that participants are brining other

factors—like values—to the decision-making process. Students taking a posttest after a

course in ethics might be likely to see an increase in the U Score because it is this rational

process of ethical decision-making that those courses attempt to teach. However, by


reframing the decision-making process in a values perspective, the experiment may have

brought the value portion of the decision-making process to the forefront impacting the

rational decision-making process. Students may have selected answers and decision

considerations that were consistent with their values and these may have conflicted with

what is consistent with rational decision-making. While outside the scope of this paper,

qualitative analysis of the value sets and decision sets might provide some insights into

the level of “values fit” versus “rational fit” of the participant’s responses. It is also

important to note that the scores dropped the most for the “10+” shared value sets

indicating that perhaps the more values perspectives shared, the more prominent values

become as a decision consideration. This might also explain the lack of significant

change at CSU. Kahneman (2011) would refer to this as values “priming”. The more you

are primed to think of values the more you will use values factors rather than “rational”

factors in matching decisions to decision considerations.

It is also possible that by framing the decision-making process in value terms that

the opportunity set—the set of alternative actions an individual can take to solve a

problem—changed for the individuals. Sen (1994) believes that when the opportunity set

is influenced by ethics, social behavior, epistemology, or other non-rational choice

influences these factors narrow the opportunity set. He refers to this process as menu

dependency; meaning when non-rational factors are added to the decision the menu of

choices becomes smaller than the opportunity set. There may have been some decision

considerations that while they were consistent with the action the individual believed he

or she would take, may not have been consistent with their values. Or, they may have

selected an action based on their values that was inconsistent with the rational decision-


making process they went through when selecting important decision considerations. It is

also important to note, that while their decision-making process became more quasi-

rational, no group made decisions that were significantly less ethical than they did when

they were using a more rational process. The MSC group actually significantly improved

their scores bringing them up to approximately the same level at CSU students.

Research Conclusions

Asking participants to reflect on and share the values they associate with decision

considerations produced some significant results. MSC students demonstrated a

statistically significant increase in their ethical decision-making ability, while all groups

changed the way they used information to make ethical decisions.

Much of the literature reviewed suggests that relationships are an important factor

when values are formed and utilized in decision-making. This research provides some

support for that assertion at least within the context of women within small groups with

relatively strong relationships in a community. Sharing a greater portion of the groups

values might also impact improved ethical decision-making, however, more research is

needed to determine if this factor also relates to gender, group size, and relationships.

Group “sameness” may be a factor in maintaining a similar level of ethical

decision-making even after a values context is introduced. This is because groups that are

already very much the same, at least in terms of cognitive development and deciding to

attend the same school, may already have similar values structures that will reinforce one

another rather than challenge values. More cognitive and value diversity might cause

others to be influenced by shared values in a group as demonstrated by the significant


improvements in ethical decision-making at MSC. This is important, because the MSC

group may be more closely representative of the general population than the CSU group.

The increase in ethical decision-making by the MSC group suggests some

interesting correlations between group size, group diversity, gender, and relationships in

decision-making. However, the most significant result of this research is the Utilizer

Score results. One goal of this research was to determine what the role of values is in

decision-making. The models of economic decision-making assume a dichotomy between

facts and values in decision-making with individuals always making decisions using

rational factors. The Utilizer Score results indicate that values do play a role in how we

make decisions. By framing decision-making in a values context and priming for values,

values considerations were brought to the forefront and the decision-makers became

more quasi-rational in their approach. Yet, in no way did it make participants worse

decision-makers—at least in the ethical context measured here. In fact, some individuals

and groups became significantly better ethical decision-makers even using an instrument

that relies on rational decision-making as the assumption. The utilization of values in

decision-making might make us more quasi-rational in our approach, but does not

necessarily make us worse decision-makers. In other words, a quasi-rational approach to

the decision-making process does not lead us to make less rational decisions. In this case,

values changed what individuals believed were important issues to consider in the

decision-making process, but did not lead them to less ethical decisions. Introducing

conscious values to the process can interrupt unconscious values schemas. As this

research is exploratory in nature, additional research will need to be done to further

isolate all the potential factors discussed here to determine their impact on economic


decision-making. The purpose of this research is to provide potential signals of direction

and hypotheses for future research.

It should be noted that values were not utilized alone, but along with the facts

given in the scenario. These results do not tell us what would happen if we asked

participants to use values alone in their decision-making. However, the combined use of

facts and values in decision-making is consistent with and supports the values inclusive

models of decision-making discussed in the literature (Bell, 2011b; Etzioni, 1988; Huei-

Chun, 2010; Subramaniam, 1963; Sen, 1994; 2004).

Limitations

One of the greatest threats to multiple group tests is the selection threat or

selection bias as this was a convenience sample, specifically the primary internal validity

issue, which is the degree to which the groups are comparable before the study. This

issue has been addressed in the results section and having groups from one university that

may have been more comparable before the study than the groups at the second

university proved to be an asset to the study. The short duration of the experiment did

minimize history, maturation, and mortality threats.

Using validated and identical instruments administered by the same researcher

across groups minimized one instrumentation threat. However, the instrument itself was

both a limitation and an asset, as it was not designed for the type of intervention utilized

here.

As with any research, Type I (false positive) and Type II (false negative) errors

were a threat. The researched attempted to minimize these threats by repeating the

experiment 5 times. As this research is exploratory, repeating the experiment in the future


to gather additional data would help further minimize these threats and provide validity

for the results discussed here.

One potential limitation to any study is that it may not be representative of the

general population. Using business students that are too similar might lead to concerns

about generalizability; however, there were a diverse group of majors in the classes. In

addition to business administration majors including human resources, entrepreneurship,

finance, economics, hospitality management, accounting, and marketing majors there

were computer science, mechanical engineering, nursing, logistics and supply chain

management, human services, music, dental hygiene, computer networking technology,

interior design and equine science majors represented in the classes. By including an

“open enrollment” campus in the experiments this does help with generalizability, as

these students are quite diverse. As a result, these findings should be generalizable to a

broader population. This study was done in classrooms on university campuses, which

leaves the potential for noise and other distractions that could impact participants.

Another limitation is that the study asked participants to describe a single value

that represented the personal values of a decision-maker who believed a particular

decision consideration was important. It does not include multiple values that might be

the optimal description. The study is also limited to the role of values in ethical decision-

making, whereas values may have a role in other aspects of decision-making.

The time required to complete the experiment also proved to be a limitation.

While I had several campuses willing to participate, individual professors and department

heads were often unwilling to give up an entire class for the experiment. This

significantly reduced my participant options. While this was a convenience sample, it


might have been preferable to have larger class sizes to more accurately analyze data for

statistical significance at the group level. However, the variety of class sizes did add

diversity and the ability to consider whether the size of the group might have impacted

group or school results.

Subject confidentiality is a potential ethical issue that was addressed by assigning

anonymous participant numbers to each participant. All results were stored in a secured

file cabinet and computer to ensure confidentiality and integrity of the data. In addition,

the study and use of student participants was reviewed and approved by the George Fox

University Human Subjects Committee. A copy of the approval is available in Appendix

H.


The literature reviewed for this study suggests, and nearly all the participants in

these experiments have stated, that they consider their personal values when making

work decisions (Brown & Trevino, 2006; Finegan, 2000; Gamble & Gibson, 1999; Jiang,

Lin, & Lin, 2010; Keltner, Langner, & Allison, 2006; Meglino, Ravlin, & Adkins, 1989;

Palmer, 2000; ; Suar & Khuntia, 2010; ). Bringing values to the conscious level can

interrupt unconscious values schemas providing support for the influence of group,

societal, environmental, and organizational values in microeconomic decision-making

creating implications for organizations and leaders. There is also evidence to suggest that

by adding a values framework to decision-making, employees may make better decisions

or, if they already share similar values, they will maintain their current level of decision-

making at least from an ethical perspective.


As this experiment was limited to ethical decision-making and values, what can

organizations and leaders learn from this research? As individual’s report bringing their

personal values to work, how can leaders in organizations encourage ethical decision-

making in groups?

One important application for leaders to learn from this research is that hiring

cognitively diverse employees can help eliminate “groupthink” by challenging the ideas

and perceptions of others from a values context. Often pre-employment tests are designed

to measure the rational or “process” thinking of individuals with hiring preferences being

given to those individuals who perform highly on these tests. In our research groups, SAT

scores were a major factor in creating the CSU group indicating relative cognitive

sameness, but also indications of groupthink as adding a values framework did not

challenge their decision-making because their decision-making processes may have

already been similar. This study indicates diverse, even quasi-rational approaches to

decision-making can result in more rational and ethical decision-making. Leaders should

look for individuals with diverse values, perceptions, and decision-making processes in

order to improve organizational decision-making and minimize groupthink.

The results of this research are consistent with sensemaking literature that

suggests placing stimuli into some kind of framework allows employees to better

comprehend problems and solutions so they may take appropriate actions. In this case we

framed and “primed” the stimuli within a values framework that helped some groups

improve their ethical decision-making. Organizations can do the same by developing

values statements and then challenging employees to consider company and personal

values in their decision-making. Ancona (2012) states that sensemaking “involves


coming up with plausible understandings and meanings; testing them with others and via,

action; and then refining our understandings or abandoning them in favor of new ones

that better explain a shifting reality” (p. 5). Organizational leaders might encourage

employees to consider and share values associated with decisions to help them either

make better ethical decisions or gain a greater level of commitment to ethical decisions

when they are made. This form of “sensegiving” that focuses on finding opportunities to

describe ethical decision-making in a way that appeals to the values of employees, can

help organizations reach strategic ethics goals (Bartunek, Krim, Necochea, &

Humphries,1999). When personal values and organizational values are brought closer

together there is greater organizational commitment that leads to less absenteeism and

turnover and improved job satisfaction, organizational citizenship, and job performance

(Finegan, 2000).

By framing decision-making in a values context, leaders in organizations may

also be able to promote congruency with the values of the organization helping front-line

employees understand how their personal values and actions can support the mission of

the organization. Sensemaking theory supports this strategy as interacting with others in a

values framework allows mental models to be tested and modified (Ancona, 2012) as we

saw in the experiment’s ability to interrupt both unconscious values schemas and

decision-making processes.

As discussed in the literature review, leaders who articulate their values and

model ethical behavior can reduce unethical behavior and interpersonal conflicts (Mayer

et al., 2012), make ethical behaviors by subordinates a habit (Almeida, 2011), and

positively impact the personal values of employees (Weiss, 1978). Having employees


working from a common “map”—in this case using values as a guide—it is easier to

coordinate consistent ethical actions across departments (Ancona, 2012).

Implications for Academicians

The study of economic decision-making has focused on removing value

judgments from the discussion of economic decision-making in favor of the rational

choice tautology. The emphasis on rational, mechanical-mathematical models was

designed to remove the “softer” social science aspects of economic thought in order to

bring the discipline closer to the hard sciences (Binmore, 2007; Nelson, 2003; Punam,

2002). Yet economic decision-making is much more complex and, as this research has

demonstrated, may be influenced by both internal and external “non-rational” factors that

disrupt the assumption of the rational actor at least in the decision-making process. If

values are included in economic decision-making, academicians will need to find ways to

build layers on top of rational mechanical-mathematical models to capture these

influences. By attempting to remove values from the decision-making process we are

essentially disconnecting individuals from their decisions, yet individual actors can

influence those around them.

The most significant implication for academicians is the need to differentiate

between the rationality of the decision-making process and rational decisions. The study

indicates the decision-making process can be less rational than traditional economic

models require, yet lead to equally or better rational decisions. Traditional rational

models require consideration of marginal cost/marginal benefit “facts” exclusively,

however, assigning personal values to the decision considerations in this study caused the

decision-making process to appear less rational, but often improved the rationality of


decisions. This appears to be especially true when a cognitively diverse group exists, as

we would expect in the general population. In diverse populations consciously adding

and recognizing values considerations in the economic decision-making process may lead

to better consensus as this research found reflecting on and sharing values moved the

groups toward more equally rational decisions in the ethical context tested here. The

literature reviewed here suggests this value inclusive process may be more consistent

with how most individuals actually make decisions in groups.

Recommendations for Future Research

This exploratory research provides evidence that gender, group size, and

relationships may impact how individuals use values in their decision-making in groups

when those values are shared. There is also evidence to support that even if the decision-

making process becomes more quasi-rational as a result of adding values to decision

considerations, this process still results in the same or better decisions. This study was

limited to an instrument that tests values schemas in ethical decision-making. Additional

research could be done using other instruments to determine if the same factors impact

other decisions. Experiments using the DIT2 that look specifically at group diversity,

gender, group size, and relationships would also be useful in validating the results of this

study as well as how the factors interact. For example, the research suggests females in

small groups with close relationships might be influenced by the values of a group more

so than when only one of the factors exists. The next step is to measure the interactions of

these factors to determine their weights and influence. Also, while the study indicates an

ability to disrupt values schemas in the very short-run, additional research needs to be

done to determine whether there is a longer-term impact to the values schema disruption.


A qualitative approach that looks for changes in decision sets based on the values

of individuals and the values set shared with the group would also provide additional

insights into how shared values impact future decisions. Do participants move toward

decision considerations that others assigned positive values to? Were they likely to give

less weight to a decision consideration that most felt demonstrated the lack of a positive

value?

As this was a quasi-experimental laboratory design, additional field studies using

a Natualistic Decision Making model (Lipshitz et al., 2001) to determine how individuals

respond to reframing decision-making in a values framework in organizational settings as

there is a risk that recognition priming could have led participants to match decision

considerations with values, but not the action participants would actually take.

Conclusion

The broad purpose of this research was to explore the role of values in economic

decision-making as presented by Bell’s (2011b) model of values inclusive economic

decision-making. To accomplish this a more narrow purpose was established to explore

the role reflecting on and sharing values plays in our individual decision-making schemas

in groups. Specifically to answer the question: Can introducing values consciously into

decision considerations disrupt existing unconscious values schemas?

To accomplish this goal a quasi-experimental pretest/posttest design was

developed using the Defining Issues 2 Test (DIT2). The DIT2 measures the unconscious

values schemas individuals use in their decision-making that determine their level of

ethical decision-making. Participants were not told the instrument measured ethical

decision-making, only that it was designed to measure how they make decisions.


Participants were given the DIT2 as a pretest and then asked to assign the values

they associated with each of the decision considerations in the first scenario. Depending

on the group, either all or random samples of 5 or 10 of the values assigned were

anonymously shared with the group. It is important to note that they were not discussed,

only shared and that this brief activity was the first time a values frame was introduced to

the decision-making process. Participants were then given the DIT2 as a posttest. The

entire process took between 1 ¼ and 1 ½ hours. Prior to running the experiments, the

instrument and process were piloted with a class at MSC.

The study found several significant results. First, reframing decision-making in a

values context made the decision process more quasi-rational for participants as a whole,

yet led to consistent or improved decisions in the ethical context tested here. Decisions

remained consistent in the CSU group where the group may have had more “sameness”

going into the experiment. This suggests that sharing values reinforced, rather than

challenged existing values schemas.

The MSC group both significantly improved Postconventional ethical decision-

making and moved away from lower level (personal interest) decision considerations.

This is significant because rational, mechanical-mathematical economic models are based

on an assumption of “self-interest”, while the study demonstrated the consideration of

values in addition to facts moved individual and group decision-making farther away

from pure self-interest in this instance This is consistent with both Bell’s (2011) and

Sen’s (1994; 2004) ideas of more holistic factors beyond self-interest in economic

decision considerations. There is evidence to suggest group diversity, gender, group size,

and relationships may have impacted the use of values and were factors in the


improvements at MSC as well, but additional research is need to add validity to these

results. The results also reflect a short-term interruption of values schemas and additional

research is needed to determine any long-term impacts. However, Bell’s model suggests

schemas are constantly changing so any measure at a point in time may yield different

results based on the influences at the time.

These results have implications for academicians. It suggests a need to add layers

on top of rational, mechanical-mathematical models to include values in descriptive and

predictive economic models. For Bell’s (2011b) theory of value-inclusive economic

decision-making it has meant adding additional factors including the external

environment, values schemas, and social learning to provide a more complete picture of

how values are developed, utilized in decision-making, and influenced/modified. It also

means distinguishing between the rationality of the decision-making process and rational

decisions as the results indicate a more quasi-rational process can still lead to more

rational decisions.

For businesses and managers, the study provides insight into how employees

might use personal values to make sense of information and situations and make

decisions at work. Bringing values to the conscious level can interrupt unconscious

values schemas providing support for the influence of group, societal, environmental, and

organizational values in microeconomic decision-making creating implications for

organizations and leaders. By framing decision-making in a values context, leaders in

organizations may also be able to promote congruency with the values of the organization

helping front-line employees understand how their personal values and actions can

support the mission of the organization. Sensemaking theory supports this strategy as


interacting with others in a values framework allows mental models to be tested and

modified (Ancona, 2012) as we saw in the experiment’s ability to interrupt both

unconscious values schemas and decision-making processes.


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Appendix A

Defining Issues Test-2 and Demographic Questions

Pre-Test















Appendix B

Defining Issues Test-2

Post-Test










Appendix C

Values/Anti-Values Prompts

Freedom/Servitude

Creativity/Uncreative

Independent/Dependent

Choosing own goals/Not choosing own

goals

Curious/Disinterested

Self-respect/Shame

An exciting life/An unexciting life

A varied life/An homogeneous life

Daring/Unadventurous

Pleasure/Dissatisfaction

Enjoying life/Disliking life

Ambitious/Unambitious

Influential/Insignificant

Capable/Unqualified

Successful/Unsuccessful

Intelligent/Unintelligent

Self-respect/Shame

Social power/Social Weakness

Wealth/Indebtedness

Authority/Powerlessness

Preserving my public image/Disregarding

my public image

Social recognition/Social disgrace

National security/National insecurity

Reciprocation of favors/Nonreciprocation of

favors

Family security/Family insecurity

Sense of belonging/sense of distance

Social order/Social disorder

Healthy/Unhealthy

Clean/Unclean

Obedient/Disobedient

Self-discipline/Unconstraint

Politeness/Rudeness

Honoring of parents and elders/Dishonoring

of parents and elders

Respect for tradition/Disrespect for tradition

Devout/Unfaithful

Accepting my portion in life/Not accepting

my portion in life

Humble/Arrogant

Moderate/Intemperate

A spiritual life/A nonspiritual life

Meaning of life/No meaning of life

Inner harmony/Inner conflict

Detachment/Bias

Helpful/Unhelpful

Responsible/Irresponsible

Forgiving/Not forgiving

Honest/Dishonest

Loyal/Disloyal

Mature love/Immature love

True friendship/False friendship

Equality/Inequality

Unity with nature/Disunity with nature

Wisdom/Imprudence

A world of beauty/A world of ugliness

Social justice/Social injustice

Broad-minded/Narrow-minded

Protecting the environment/Harming the

environment

A world at peace/A world in disharmony


Appendix D

Values Expressed

Participant number_________________

Example

Imagine you are about to vote for a candidate for the Presidency of

the United States. Before you vote, you are asked to rate the

importance of five issues you could consider in deciding who to vote for.

Decision Consideration Value Expressed

Financially are you personally better off now than you _______________

were four years ago?

Does one candidate have a superior moral character? _______________

Which candidate stands the tallest? _______________

Which candidate would make the best world leader? _______________

Which candidate has the best ideas for our country’s internal

Problems, like crime and health care. _______________


Story 1-Famine

The small village in northern India has experienced shortages of food before, but this year’s

famine is worse than ever. Some families are even trying to feed themselves by making soup

from tree bark. Mustaq Singh’s family is near starvation. He has heard that a rich man in his

village has supplies of food stored away and is hording food while its price goes higher so that he

can sell the food later at a huge profit. Mustaq is desperate and thinks about stealing some food

from the rich man’s warehouse. The small amout of food that he needs for his family probably

wouldn’t even be missed.

Decision Consideration Value Expressed

Is Mustaq Singh courageous enough to risk getting caught for

stealing? _______________

Isn’t it only natural for a loving father to care so much for his family

that he would steal? _______________

Shouldn’t the community’s laws be upheld? _______________

Does Mustaq Singh know a good recipe for preparing soup from

tree bark? _______________

Does the rich man have any legal right to store food when other

people are starving? _______________

Is the motive of Mustaq Singh to steal for himself or to steal for

his family? _______________

What values are going to be the basis for social cooperation? _______________

Is the epitome of eating reconcilable with the culpability of

stealing? _______________

Does the rich man deserve to be robbed for being so greedy? _______________

Isn’t private property an institution to enable the rich to exploit

the poor? _______________

Would stealing bring about more total good for everybody concerned

or wouldn’t it? _______________

Are laws getting in the way of the most basic claim of any member

of society? _______________


Appendix E

Shared Values Set: Group 1 Decision Consideration Value Expressed

Is Mustaq Singh courageous

enough to risk getting

caught for stealing?

Responsible, daring, dishonest, capable, disobedient, daring,

disliking life, loyalty, disregarding public image, self-respect,

courage

Isn’t it only natural for a

loving father to care so

much for his family that he

would steal?

Responsible, health, social disorder, family security, family

insecurity, family loyalty, devout

Shouldn’t the community’s

laws be upheld?

Unhealthy, authority, disobedience, protecting the environment,

social justice, social order, not accepting portion in life, servitude

Does Mustaq Singh know a

good recipe for preparing

soup from tree bark?

Capable, helpful, creativity, unhealthy, world of ugliness, health,

insignificant, healthy

Does the rich man have any

legal right to store food

when other people are

starving?

Inner conflict, equality, wealth, social injustice, detachment, disunity

with nature, social justice, bias

Is the motive of Mustaq

Singh to steal for himself or

to steal forhis family?

Family security, forgiving, shame, irresponsible, responsible,

reciprocation of favors, immature love, mature love, social justice

What values are going to be

the basis for social

cooperation?

Forgiving, world at peace, self-respect, social justice, responsible,

authority, accepting my portion in life, wealth, social injustice

Is the epitome of eating

reconcilable with the

culpability of stealing?

Self respect, shame, social justice, social order, health, pleasure,

arrogant, inner conflict, disloyal, dishonest, shame, narrow mind

Does the rich man deserve

to be robbed for being so

greedy?

Rudeness, social justice, social injustice, wealth, social order,

dishonesty, moderate, accepting portion in life, social power

Isn’t private property an

institution to enable the rich

to exploitthe poor?

Unhelpful, not accepting one’s portion in live, social order, shame,

wealth narrow minded, social justice, irresponsible, inequality,

social weakness, security, equality, social injustice

Would stealing bring about

more total good for

everybody concerned or

wouldn’t it?

Responsible, social injustice, authority, a world in disharmony,

social weakness, independent, equality, unhelpful, sense of distance,

inequality, helpful, social order

Are laws getting in the way

of the most basic claim of

any memberof society?

Freedom, powerlessness, social power, social justice, social

injustice, unhelpful, meaning of life, broad minded, honest, social

disorder



Is Mustaq Singh courageous

enough to risk getting caught

for stealing?

Ambitious, family security, powerlessness, social recognition,

daring, arrogant, excepting my portion in live, loyal




would steal?

Family security, responsible, devout, healthy


laws be upheld?

Social justice, social order, obedient, social weakness, inequality



soup from

tree bark?

Not choosing own goals, creativity, excepting portion in life,

insignificant, helpful, capable



when other

people are starving?

Helpful, authority, wealth, social power, unhelpful, social justice,

equality



to steal for his family?

Family security, meaning of life, self respect, responsible, inner

conflict, detachment



cooperation?

Social order, social justice, authority, wealth, a world at peace,

influential, social injustice, equality




Honest, wisdom, narrow minded, forgiving, unhealthy, reciprocation

of favors, not accepting portion in life, social justice, a world of

ugliness, responsible

Does the rich man deserve to

be robbed for being so

greedy?

Arrogance, social justice, politeness, dishonest, social injustice,

social power, a world of ugliness, inequality



to exploit the poor?

Social injustice, wealth, bias, humble, equality, inequality, freedom


more total good for

everybody concerned

or wouldn’t it?

Social order, a world at peace, inequality, respect for tradition, a

world in disharmony, unhelpful, helpful



any member of society?

Social justice, dissatisfaction, equality, family security, obedience,

social disorder, protect the environment, social order, choosing own

goals



Is Mustaq Singh

courageous enough to risk

getting caught for stealing?

Dishonesty, family security, unintelligent, social disorder, capable




would steal?

Powerlessness, shame, family security, choosing own goals, self-

respect


laws be upheld?

Social justice, shame, servitude, social order, quality



soup from

tree bark?

Unhelpful, insignificant, arrogant, unintelligent



when other


Disloyal, wealth, successful, freedom, social power




Responsible, loyal, social order, self-respect



cooperation?

Healthy, honesty, social justice, social power




Shame, influential, family security, self-respect



greedy?

Social power, unclean, honesty, authority




Accepting my portion in life, insignificant, broad minded, social

order, imprudence


more total good for

everybody concerned

or wouldn’t it?

Unhealthy, social disgrace, social justice, social recognition




The world in disharmony, social justice, national security, authority



Is Mustaq Singh



Meaning of life, daring, capable, family security, health




would steal?

Inner harmony, family security, authority, daring


laws be upheld?

Social justice, social injustice, authority, obedient, social order



soup from

tree bark?

Creativity, unhealthy, healthy, harming the environment



when other


Social power, powerless, social disgrace, authority, social order




Humble, sense of belonging, family security, unfaithful



cooperation?

Equality, influential, powerlessness, social recognition




Detachment, accepting portion in life, equality, social disgrace



greedy?

Arrogant, indebtedness, wealth




Disorder, inequality, uninitelligent


more total good for

everybody concerned

or wouldn’t it?

Social order, equality, a world of beauty, disobedient




World of ugliness, obedient, enjoying life, freedom, insignificant



Is Mustaq Singh



Family security, meaning of life, responsible




would steal?

Family security, responsible


laws be upheld?

Social power, equality, social order, authority, freedom



soup from tree bark?

Intelligent, creativity, independence



when other


Intelligent, social justice, social injustice, social order, shame




Family security, humble



cooperation?

Social order, helpful, equality, social recognition




Accepting portion in life, inner conflict, inner harmony, social

injustice



greedy?

Wealth, bias, social disorder, not accepting my portion in life




Social order, capability, social injustice, inner conflict


more total good for

everybody concerned

or wouldn’t it?

Social disorder, honest, world of beauty




Authority, inequality, freedom


Appendix F

N2 Score Descriptive Statistics and t-Test Results by Group

N2Score Pre 1 N2Score Post 1

Mean 32.4707919 Mean 34.37175236


Median 36.66027801 Median 36.21419279

Mode #N/A Mode #N/A

Standard Deviation 10.63878455

Standard



Kurtosis 2.377987165 Kurtosis

-

0.393679647

Skewness -1.478004326 Skewness

-

0.573774133

Range 35.39136075 Range 41.5751324

Minimum 7.020660722 Minimum 12.0095456

Maximum 42.41202148 Maximum 53.58467801

Sum 357.1787109 Sum 378.089276

Count 11 Count 11


Mean 26.14421447 Mean 30.27666326


Median 26.69264657 Median 24.90875872

Mode #N/A Mode #N/A


Standard



Kurtosis -0.973004056 Kurtosis

-

1.452984637


Range 50.39917637 Range 51.9509142

Minimum 1.662813234 Minimum 6.446759816

Maximum 52.06198961 Maximum 58.39767402

Sum 522.8842893 Sum 605.5332652

Count 20 Count 20



Mean 27.13117425 Mean 33.23663206


Median 27.79754756 Median 33.68428625

Mode #N/A Mode #N/A


Standard



Kurtosis -1.356869269 Kurtosis 0.530728906


Range 32.5451282 Range 49.19446038

Minimum 11.69458314 Minimum 15.08192823

Maximum 44.23971134 Maximum 64.2763886

Sum 461.2299622 Sum 565.0227451

Count 17 Count 17


Mean 28.30691515 Mean 21.52186151


Median 30.27532653 Median 19.99945096

Mode #N/A Mode #N/A




-

0.872555273

Skewness -0.108285866 Skewness 0.174020636

Range 45.05154414 Range 44.10436412

Minimum 5.300049497 Minimum 1.186909712

Maximum 50.35159364 Maximum 45.29127383

Sum 679.3659637 Sum 516.5246762

Count 24 Count 24

N2 Score Pre 5 N2 Score Post 5

Mean 36.21427423 Mean 38.29193961


Median 36.88693907 Median 38.21388959


Mode #N/A Mode #N/A


Standard




-

0.851866224


Range 61.03082413 Range 63.07693037

Minimum 5.412950513 Minimum 9.906068215

Maximum 66.44377464 Maximum 72.98299859

Sum 1267.499598 Sum 1340.217886

Count 35 Count 35


t-Test: Paired Two Sample for

Means N2 Score Group 1

Variable 1 Variable 2

Mean 32.4707919 34.37175236

Variance 113.1837367 173.4532337

Observations 11 11

Pearson Correlation 0.805707725

Hypothesized Mean Difference 0

df 10

t Stat -0.808209657

P(T<=t) one-tail 0.218886928

t Critical one-tail 1.812461123

P(T<=t) two-tail 0.437773855

t Critical two-tail 2.228138852

t-Test: Paired Two Sample for Means

N2 Score Group 2


Mean 26.14421447 30.27666326

Variance 205.2052573 275.627874

Observations 20 20


Hypothesized Mean

Difference 0

df 19

t Stat

-

2.662772899

P(T<=t) one-tail 0.007686697


P(T<=t) two-tail 0.015373394



N2 Score Group 3


Mean 27.13117425 33.23663206

Variance 133.9114562 165.5713601

Observations 17 17


Hypothesized Mean

Difference 0

df 16


t Stat

-

2.052247254

P(T<=t) one-tail 0.028440102


P(T<=t) two-tail 0.056880204



N2 Score Group 4


Mean 28.30691515 21.52186151

Variance 123.7367227 153.6609899

Observations 24 24


Hypothesized Mean

Difference 0

df 23

t Stat 2.426383951

P(T<=t) one-tail 0.011748508


P(T<=t) two-tail 0.023497015



N2 Score Group 5


Mean 36.21427423 38.29193961

Variance 246.2611538 293.8104638

Observations 35 35


Hypothesized Mean

Difference 0

df 34

t Stat -1.5153403

P(T<=t) one-tail 0.069464435


P(T<=t) two-tail 0.13892887



Appendix G

Stage 23 and 4P Descriptive Statistics and t-Tests

Stage 23 Descriptive By Group

Stage 23 Pre Group 1 Stage 23 Post Group 1

Mean 28.36363636 Mean 24.36363636 Standard Error 2.49826386 Standard Error 3.878314365 Median 30 Median 20 Mode 32 Mode 20 Standard Deviation 8.285803851


Sample Variance 68.65454545 Sample Variance 165.4545455 Kurtosis 1.059282758 Kurtosis 1.686852635 Skewness 0.502441968 Skewness 1.253477284 Range 30 Range 44 Minimum 16 Minimum 10 Maximum 46 Maximum 54 Sum 312 Sum 268

Count 11 Count 11


Mean 27.6 Mean 27.6 Standard Error 3.164274258 Standard Error 3.45984484 Median 23 Median 25

Mode 12 Mode 32 Standard Deviation 14.15106468



Kurtosis -

0.129339419 Kurtosis -

0.436354486 Skewness 0.825606231 Skewness 0.576714106

Range 48 Range 54 Minimum 12 Minimum 8 Maximum 60 Maximum 62 Sum 552 Sum 552

Count 20 Count 20






Kurtosis -

1.190000107 Kurtosis -

1.116419022

Skewness -

0.314918089 Skewness -

0.115266434 Range 40 Range 44 Minimum 10 Minimum 4 Maximum 50 Maximum 48 Sum 484 Sum 452

Count 16 Count 17






Kurtosis -

0.128868487 Kurtosis -0.68507537

Skewness -

0.390785004 Skewness -

0.588717017 Range 36 Range 42 Minimum 14 Minimum 12 Maximum 50 Maximum 54

Sum 794 Sum 852

Count 24 Count 24


Stage 23 Pre Group 5 Stage 23 Pre Group 5




Kurtosis -

0.971649409 Kurtosis -

0.583931879 Skewness 0.332948282 Skewness 0.337345415


Count 35 Count 35

Stage 23 Descriptive By School

Stage 23 Pre MSC Stage 23 Post MSC

Mean 28.625 Mean 26.5 Standard Error 1.791584197 Standard Error 2.035587635

Median 28 Median 26 Mode 32 Mode 32 Standard Deviation 12.41245942



Kurtosis -

0.482283949 Kurtosis -

0.503608453 Skewness 0.386954188 Skewness 0.468350003 Range 50 Range 58 Minimum 10 Minimum 4 Maximum 60 Maximum 62

Sum 1374 Sum 1272

Count 48 Count 48

Stage 23 Descriptive All Data

Stage 23 Pre All Data Stage 23 PostAll Data

Mean 28.52336449 Mean 27.8317757 Standard Error 1.17648639 Standard Error 1.326618413


Median 28 Median 26




Kurtosis -0.7652174 Kurtosis -

0.883736879 Skewness 0.138215681 Skewness 0.19703414 Range 54 Range 58 Minimum 6 Minimum 4 Maximum 60 Maximum 62 Sum 3052 Sum 2978

Count 107 Count 107

Stage 23 t-Test By Group


Stage 23 Group 1


Mean 28.36363636 24.36363636 Variance 68.65454545 165.4545455 Observations 11 11 Pearson Correlation 0.831815499 Hypothesized Mean Difference 0 df 10 t Stat 1.760281668 P(T<=t) one-tail 0.05442699 t Critical one-tail 1.812461123 P(T<=t) two-tail 0.10885398



Stage 23 Group 2


Mean 27.6 27.6 Variance 200.2526316 239.4105263 Observations 20 20


Pearson Correlation 0.845343725 Hypothesized Mean Difference 0 df 19 t Stat 4.76546E-17 P(T<=t) one-tail 0.5 t Critical one-tail 1.729132812 P(T<=t) two-tail 1



Stage 23 Group 3


Mean 30 26.58823529 Variance 168.5 191.8823529 Observations 17 17 Pearson Correlation 0.695172434 Hypothesized Mean Difference 0 df 16 t Stat 1.338917165 P(T<=t) one-tail 0.099655534 t Critical one-tail 1.745883676 P(T<=t) two-tail 0.199311069



Stage 23 Group 4


Mean 33.08333333 35.5 Variance 90.07971014 146.5217391 Observations 24 24 Pearson Correlation 0.321303408 Hypothesized Mean Difference 0

df 23

t Stat -

0.927957396 P(T<=t) one-tail 0.181533137 t Critical one-tail 1.713871528 P(T<=t) two-tail 0.363066274




Stage 23 Group 5


Mean 25.25714286 24.4 Variance 162.1378151 156.8941176 Observations 35 35 Pearson Correlation 0.841334993 Hypothesized Mean Difference 0 df 34 t Stat 0.712482319

P(T<=t) one-tail 0.240513679 t Critical one-tail 1.690924255 P(T<=t) two-tail 0.481027358


Stage 23 t-Test By School


Stage 23 MSC


Mean 28.625 26.5 Variance 154.0691489 198.893617

Observations 48 48 Pearson Correlation 0.776545246 Hypothesized Mean Difference 0 df 47 t Stat 1.634913438 P(T<=t) one-tail 0.054374096 t Critical one-tail 1.677926722 P(T<=t) two-tail 0.108748192



Stage 23 CSU


Mean 28.44067797 28.91525424 Variance 145.8024547 180.3202805


Observations 59 59

Pearson Correlation 0.706922165 Hypothesized Mean Difference 0 df 58

t Stat -



Stage 23 t-Test All Data


Stage 23 All Data


Mean 28.52336449 27.8317757 Variance 148.100864 188.3110563 Observations 107 107 Pearson Correlation 0.735815926 Hypothesized Mean Difference 0 df 106

t Stat 0.751376624 P(T<=t) one-tail 0.227045501 t Critical one-tail 1.659356034 P(T<=t) two-tail 0.454091002


4P Descriptive By Group

4P Pre Group 1 4P Post Group 1





Sample Variance 119.4181818 Sample Variance 228.6545455 Kurtosis 0.230159095 Kurtosis -0.9986119 Skewness 0.99486071 Skewness 0.155499382 Range 34 Range 46 Minimum 22 Minimum 14 Maximum 56 Maximum 60 Sum 382 Sum 392

Count 11 Count 11





Kurtosis -

0.129645373 Kurtosis -

0.751960456

Skewness -

0.447850824 Skewness -

0.373060661 Range 52 Range 40 Minimum 4 Minimum 12 Maximum 56 Maximum 52 Sum 658 Sum 688

Count 20 Count 20


Mean 36.11764706 Mean 36 Standard Error 4.106918797 Standard Error 3.910769444 Median 38 Median 32 Mode 42 Mode 40 Standard 16.93326 Standard 16.1245155


Deviation Deviation

Sample Variance 286.7352941 Sample Variance 260 Kurtosis 0.047674091 Kurtosis 0.953722739 Skewness 0.46247221 Skewness 1.098900043 Range 64 Range 60 Minimum 10 Minimum 16 Maximum 74 Maximum 76 Sum 614 Sum 612

Count 17 Count 17





Kurtosis -

0.290422452 Kurtosis -

0.376249665 Skewness 0.049015264 Skewness 0.087705898 Range 52 Range 48 Minimum 4 Minimum 14

Maximum 56 Maximum 62 Sum 772 Sum 846

Count 24 Count 24

4P Pre Group 5 4P Pre Group 5

Mean 30.45714286 Mean 30.05714286 Standard Error 2.257515127 Standard Error 2.502416599 Median 30 Median 28 Mode 18 Mode 20 Standard 13.3556396 Standard 14.80449625


Deviation Deviation


Kurtosis -

0.723399491 Kurtosis -


Count 35 Count 35

4P Descriptive By School

4P Pre MSC 4P Post MSC



Sample Variance 207.8705674 Sample Variance 196.3617021 Kurtosis 0.246005245 Kurtosis 0.188115249 Skewness 0.188766611 Skewness 0.510466575


Count 48 Count 48

4P Pre 4 CSU 4P Post 4 CSU

Mean 31.15254237 Mean 32.16949153 Standard Error 1.716426902 Standard Error 1.827952412 Median 30 Median 34 Mode 18 Mode 38 Standard 13.1841252 Standard 14.04076889


Deviation Deviation


Kurtosis -0.68131744 Kurtosis -


Count 59 Count 59

4P Descriptive All Data

4P Pre All Data 4P Post All Data




Kurtosis -

0.229249449 Kurtosis -


Count 107 Count 107

4P t-Test By Group


4P Group 1


Mean 34.72727273 35.63636364 Variance 119.4181818 228.6545455


Observations 11 11


t Stat -




4P Group 2



t Stat -

0.874113875




4P Group 3

Variable 1 Variable

2

Mean 36.11764706 36 Variance 286.7352941 260 Observations 17 17


Pearson Correlation 0.783766186 Hypothesized Mean Difference 0 df 16 t Stat 0.044515947 P(T<=t) one-tail 0.482521995 t Critical one-tail 1.745883676 P(T<=t) two-tail 0.96504399



4P Group 4



t Stat -

1.393156989 P(T<=t) one-tail 0.088446019 t Critical one-tail 1.713871528

P(T<=t) two-tail 0.176892038



4P Group 5


Mean 30.45714286 30.05714286 Variance 178.3731092 219.1731092 Observations 35 35 Pearson Correlation 0.791224536 Hypothesized Mean Difference 0 df 34 t Stat 0.257192263


P(T<=t) one-tail 0.399290258

t Critical one-tail 1.690924255 P(T<=t) two-tail 0.798580517


4P t-Test By School


4P MSC


Mean 34.45833333 35.25 Variance 207.8705674 196.3617021

Observations 48 48


t Stat -




4P CSU


Mean 31.15254237 32.16949153

Variance 173.8211572 197.1431911 Observations 59 59 Pearson Correlation 0.733976524 Hypothesized Mean Difference 0 df 58 t Stat -


0.784182628



4P t-Test All Data


4P All Data


Mean 32.63551402 33.55140187

Variance 190.0074061 197.3062952

Observations 107 107 Pearson Correlation 0.76371659 Hypothesized Mean Difference 0 df 106

t Stat -




Appendix H

George Fox University Human Subjects Committee Approval

From: BeckyJensen<[email protected]> Subject: HSRC Approval

Date: April 2, 2013 4:31:36 PM AKDT

To: "HollyBell(me.com)"<[email protected]>

Cc: Paul Shelton <[email protected]>

Hi Holly,

I have good news for you! I just received word that your HSRC has been approved! The form couldn't be sent today because of computer problems, but someone from that committee gave me a call to let me know it had been approved and you could move on with your research.

I apologize for the length of time you've had to wait, but at least now it's done. I will send you the form with the approval signatures as soon as I receive it.

--

Becky Jensen

Administrative Assistant

GFU School of Business

503-554-2821

Documents

An Exploratory Study of the Role ... - George Fox University